CN111523914B - User satisfaction evaluation method, device and system and data display platform - Google Patents

User satisfaction evaluation method, device and system and data display platform Download PDF

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CN111523914B
CN111523914B CN201910044825.6A CN201910044825A CN111523914B CN 111523914 B CN111523914 B CN 111523914B CN 201910044825 A CN201910044825 A CN 201910044825A CN 111523914 B CN111523914 B CN 111523914B
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emotion
emotion data
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CN111523914A (en
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康杨杨
孙常龙
刘晓钟
司罗
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Alibaba Group Holding Ltd
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Abstract

The application discloses a user satisfaction evaluation method, a user satisfaction evaluation device, a user satisfaction evaluation system and a data display platform. Wherein the method comprises the following steps: acquiring at least one comment data aiming at a product in the process of product transaction; acquiring emotion data and emotion data aiming at a product from at least one piece of comment data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the evaluated product is evaluated; and determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product. The method solves the technical problem that the existing method for carrying out static analysis on the product based on the historical data cannot dynamically reflect service improvement changes and trends of merchants.

Description

User satisfaction evaluation method, device and system and data display platform
Technical Field
The application relates to the field of internet, in particular to a user satisfaction evaluation method, a device, a system and a data display platform.
Background
With the rapid development of internet technology, online shopping becomes a main channel for people to consume daily. The merchant provides the commodity to the consumer through the network platform, the consumer can feed back the satisfaction degree of the product in the product transaction process through the network, and meanwhile, the consumer can also determine whether to purchase the product of the merchant according to the satisfaction degree fed back by other consumers. Therefore, the satisfaction degree of the consumer on the product transaction determines whether the network platform can generate enough viscosity to attract the consumer to repurchase, determines whether the consumer trusts the platform, and further determines whether the network platform can develop benign for a long time, so that the satisfaction degree of the consumer on the product transaction is very important reference information of the network platform.
Consumer satisfaction with the transaction is typically reflected by analyzing the user's evaluation of the product. However, most of the existing satisfaction analysis methods are static analysis based on historical data, for example, the evaluation display interface shown in fig. 1 is schematic, the score "4.8" consistent with the description in fig. 1 can reflect the satisfaction of the consumer on the product laterally, and according to the comment classification content (for example, "mobile phone is good", "signal is not good", etc.), the existing technology only performs static analysis on the historical data of the product, and cannot perform overall analysis on the product from the time dimension, cannot feedback the satisfaction of the consumer on the transaction of the product as a whole, and cannot dynamically reflect the improvement made by the service of the seller and the service trend, for example, cannot reflect whether the seller improves on the service attitude.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the application provides a user satisfaction evaluation method, device and system and a data display platform, which at least solve the technical problem that the existing method for carrying out static analysis on products based on historical data cannot dynamically reflect service improvement changes and trends of merchants.
According to one aspect of the embodiment of the application, there is provided a method for evaluating user satisfaction, including: acquiring at least one comment data aiming at a product in the process of product transaction; acquiring emotion data and emotion data aiming at a product from at least one piece of comment data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the evaluated product is evaluated; and determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product.
According to another aspect of the embodiment of the present application, there is also provided a method for evaluating user satisfaction, including: displaying at least one comment data of a product in the process of product transaction, wherein the at least one comment data at least comprises emotion data and emotion data, the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the product is evaluated; and displaying satisfaction data for the product, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product, and the satisfaction is determined according to the emotion data and the emotion data.
According to another aspect of the embodiments of the present application, there is also provided a data display platform, including: the processing unit is used for aiming at least one comment data of the product in the product transaction process, acquiring emotion data and emotion data aiming at the product from the at least one comment data, and then determining satisfaction data of the product based on the emotion data and the emotion data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, the emotion data is used for representing the emotion grade generated when the product is evaluated, and the satisfaction data is used for representing the change trend of the satisfaction in the transaction process; and the display unit is used for displaying the satisfaction data.
According to another aspect of the embodiment of the present application, there is further provided an apparatus for evaluating user satisfaction, including: the acquisition module is used for acquiring at least one comment data aiming at the product in the product transaction process; the first determining module is used for acquiring emotion data and emotion data aiming at the product from at least one piece of comment data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the product is evaluated; and the second determining module is used for determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product.
According to another aspect of the embodiments of the present application, there is further provided a storage medium, where the storage medium includes a stored program, and when the program runs, the device where the storage medium is controlled to execute the method for evaluating user satisfaction.
According to another aspect of the embodiments of the present application, there is also provided a computer device, including a processor, where the processor is configured to run a program, and the program executes an evaluation method of user satisfaction during running.
According to another aspect of the embodiment of the present application, there is also provided a system for evaluating user satisfaction, including: a processor; and a memory, coupled to the processor, for providing instructions to the processor for processing the steps of: acquiring at least one comment data aiming at a product in the process of product transaction; acquiring emotion data and emotion data aiming at a product from at least one piece of comment data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the evaluated product is evaluated; and determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product.
In the embodiment of the application, a manner of determining the change trend of satisfaction in the process of trading a product by using the satisfaction data of the product is adopted, in the process of trading the product, after at least one piece of comment data for the product is obtained, emotion data and emotion data for the product are obtained from the at least one piece of comment data, and then the change trend of satisfaction in the process of trading the product is determined based on the emotion data and the emotion data.
In the process, the emotion data and the emotion data affecting the shopping experience of the consumer in the product transaction process are comprehensively considered, and the satisfaction degree of the consumer on the product transaction can be accurately obtained through comprehensive analysis of the emotion data and the emotion data. In addition, after the satisfaction degree of the consumer on the product transaction is obtained, the change trend of the satisfaction degree of the product transaction along with time is further determined. Compared with the prior art, the change trend of the satisfaction degree along with time reflects the service improvement of the merchant, and the purpose of dynamically analyzing the product is achieved, so that the technical effect of dynamically reflecting the service improvement trend of the merchant is achieved, and the technical problem that the conventional method for statically analyzing the product based on historical data cannot dynamically reflect the service improvement change and trend of the merchant is solved.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a schematic diagram of an assessment display interface according to the prior art;
FIG. 2 is a block diagram of hardware architecture of an alternative method for evaluating user satisfaction in accordance with an embodiment of the present application;
FIG. 3 is a flowchart of a method for evaluating user satisfaction according to an embodiment of the present application;
FIG. 4 is a flow chart of an alternative determination of satisfaction data, according to an embodiment of the present application;
FIG. 5 is an alternative emotion profile based on emotion data according to an embodiment of the present application;
FIG. 6 is an alternative emotion data-based satisfaction curve according to an embodiment of the present application;
FIG. 7 is a schematic illustration of an alternative consumer emotion degree trend in accordance with an embodiment of the present application;
FIG. 8 is a flowchart of a method for evaluating user satisfaction according to an embodiment of the present application;
FIG. 9 is a schematic diagram of a data display platform according to an embodiment of the present application;
FIG. 10 is a schematic diagram of an apparatus for evaluating user satisfaction in accordance with an embodiment of the present application; and
Fig. 11 is a block diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to embodiments of the present application, there is also provided an embodiment of a method for evaluating user satisfaction, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
The method embodiment provided in the first embodiment of the present application may be executed in a mobile terminal, a computer terminal or a similar computing device. Taking the operation on a computer terminal as an example, fig. 2 is a hardware block diagram of a computer terminal of a user satisfaction evaluating method according to an embodiment of the present application. As shown in fig. 2, the computer terminal 10 may include one or more (only one is shown in the figure) processors 102 (the processors 102 may include, but are not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 2 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 2, or have a different configuration than shown in FIG. 2.
The memory 104 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the user satisfaction evaluation method in the embodiment of the present application, and the processor 102 executes the software programs and modules stored in the memory 104, thereby executing various functional applications and data processing, that is, implementing the user satisfaction evaluation method described above. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 106 is arranged to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
Under the above operation environment, the present application provides a method for evaluating user satisfaction as shown in fig. 3. Fig. 3 is a flowchart of a method for evaluating user satisfaction according to a first embodiment of the present application, as can be seen from fig. 3, the method includes the following steps:
step S302, at least one comment data for a product in the product transaction process is obtained.
It should be noted that, in this embodiment, an execution body for executing the evaluation method of user satisfaction may be an e-commerce platform, and the e-commerce platform may obtain at least one comment data of a consumer on a product in a product transaction process. Specifically, after the consumer purchases the product and confirms that the product is received, the service attitude, the product quality, the logistics speed and the like of the merchant can be evaluated in the product transaction process through a feedback inlet provided by the electronic commerce platform.
Optionally, the at least one piece of comment data for the product includes at least one of: text, images, video, etc.
Step S304, emotion data and emotion data for the product are obtained from at least one piece of comment data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the product is evaluated.
It should be noted that, the emotion data of the consumer for the product includes three aspects of data, namely positive emotion data, neutral emotion data and negative emotion data, where the emotion data of each aspect may further include multiple levels, each level corresponds to a different emotion score, for example, the positive emotion data has three levels, where the emotion intensity of the first level is greater than the emotion intensity of the second level, and the emotion data corresponding to the first level is greater than the emotion data corresponding to the second level; the consumer's mood data for the product may include mood data of five aspects, namely happiness, sadness and sadness. The emotional data of each aspect may also include multiple levels, for example, the emotional data corresponding to the emotion includes three levels, and the emotional intensity of the first level is greater than the emotional intensity of the second level. Alternatively, in the comment data of "bad package quality i buy today," bad package-quality "is attribute emotion analysis," package "represents attribute words," bad quality "represents emotion information of consumers," bad mood "represents emotion information of consumers," bad package quality i buy today "represents cause of bad emotion of consumers.
Optionally, in the present application, the emotion data of the consumer for the product may be obtained from at least one piece of comment data by using a sequence labeling manner, and the emotion data of the consumer for the product may be obtained from at least one piece of comment data by using a neural network model.
In an alternative scheme, currently, when a part of consumers comment on a product transaction, comments irrelevant to the product transaction are input in comment content, so that before emotion data and emotion data of the consumers for the product are obtained from comment data, an electronic commerce platform filters at least one piece of comment data to remove comment data which does not meet preset conditions, wherein the preset conditions at least comprise content of the product transaction in the comment data. Alternatively, the comment data may be filtered by means of machine learning training.
Step S306, satisfaction data of the product is determined based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product.
It should be noted that, the satisfaction degree in the process of trading the product at least includes: product quality satisfaction, logistics satisfaction and service attitude satisfaction, and emotion data at least comprise: the service data, the logistics data and the quality data of the products of the objects are preset. The preset object is a merchant, service data of the preset object reflects service attitudes of the merchant, logistics data reflects logistics speed, and quality data of products reflects product quality of products purchased by consumers.
Optionally, after the emotion data and the emotion data, the e-commerce platform can calculate and obtain product quality satisfaction according to quality data and corresponding emotion data of the product, calculate and obtain logistics satisfaction according to logistics data and corresponding emotion data, calculate and obtain service attitude satisfaction according to service data of a preset object and corresponding emotion data, then perform weighted average on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction, so as to obtain satisfaction in the process of trading the product, and then display the satisfaction in the time dimension, so as to obtain the change trend of the satisfaction in the process of trading the product. The display form of the satisfaction data can be, but is not limited to, a table and a graph.
It should be noted that the satisfaction data of the product reflects the satisfaction degree of the consumer on the product transaction, and also reflects the service quality trend of the merchant. For merchants with continuously improved and improved quality of service, the e-commerce platform can be affirmed and supported. For merchants with continuously reduced service quality, the e-commerce platform can follow up and process in time.
Based on the above-mentioned schemes defined in step S302 to step S306, it can be known that the method of determining the trend of change of satisfaction in the process of trading the product by using the satisfaction data of the product is adopted, in the process of trading the product, after at least one comment data for the product is obtained, emotion data and emotion data for the product are obtained from the at least one comment data, and then the trend of change of satisfaction in the process of trading the product is determined based on the emotion data and emotion data.
It is easy to notice that, this application has taken into consideration emotion data and emotion data that influence consumer's shopping experience in the product transaction process comprehensively, through carrying out comprehensive analysis to emotion data and emotion data, can obtain accurate consumer's satisfaction to the product transaction. In addition, after the satisfaction degree of the consumer on the product transaction is obtained, the change trend of the satisfaction degree of the product transaction along with time is further determined. Compared with the prior art, the change trend of the satisfaction degree along with time reflects the service improvement of the merchant, and the purpose of dynamically analyzing the product is achieved, so that the technical effect of dynamically reflecting the service improvement trend of the merchant is achieved, and the technical problem that the conventional method for statically analyzing the product based on historical data cannot dynamically reflect the service improvement change and trend of the merchant is solved.
It should be noted that, after obtaining at least one piece of comment data of the consumer for the product, the electronic commerce platform may obtain emotion data and emotion data for the product from the at least one piece of comment data.
Optionally, the e-commerce platform may extract an attribute word of the product from at least one piece of comment data of the product, and then obtain emotion data for the product from the at least one piece of comment data based on the attribute word. Wherein, attribute word characterizes the attribute of product, and the attribute includes at least: the service attitude, the logistics speed and the product quality of the object are preset. For example, in a comment that "boss attitudes are good and delivery is quick," attitudes "are attribute words, corresponding attributes thereof are service attitudes," delivery "is also an attribute word, and corresponding attributes thereof are logistics speeds.
In an alternative scheme, the e-commerce platform extracts attribute words of the product from at least one piece of comment data of the product based on the sequence annotation model. Specifically, the e-commerce platform processes at least one piece of comment data based on a sequence labeling model to obtain an attribute word, wherein the sequence labeling model is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises the attribute word and a label corresponding to the attribute word.
It should be noted that the sequence labeling model includes, but is not limited to, CRF (Conditional Random Field ) model, bi-LSTM (Bi-Long-Short Term Memory, two-way Long-short term memory) model.
Alternatively, description will be given taking "boss attitude is good, shipment is quick" as an example. After the electronic commerce platform inputs the comment data into the sequence labeling model, the sequence labeling model labels the boss with good attitudes and the shipment with fast, for example, labels the boss with B1, labels the boss with M1, labels the good with E1, labels the shipment with M2, labels the quick with E2, classifies the labels, wherein the label B1 is a first type, the labels M1 and M2 are a second type, and the labels E1 and E2 are a third type, and then the sequence labeling model extracts words corresponding to the second type label from the comment data, so that the attribute words can be obtained.
After determining the attribute word, the e-commerce platform performs emotion analysis on at least one piece of comment data based on the attribute word to obtain emotion data. The emotion analysis mainly comprises two analysis modes, namely an analysis mode based on a classification model and an analysis mode based on a depth model.
Optionally, in an analysis mode based on a classification model, the e-commerce platform determines first information from at least one piece of comment data, and then processes the first information based on the classification model to obtain an evaluation level for a product to obtain emotion data, wherein the classification model is trained by using multiple groups of data through machine learning, each group of data in the multiple groups of data comprises the first information and a label corresponding to the evaluation level, and the first information is information associated with an attribute word in the at least one piece of comment data. For example, in the comment that "baby is good and fabric is soft", the attribute word is determined to be "fabric" by the sequence labeling model, after the attribute word is determined, the classification model extracts words around the attribute word to obtain a plurality of words, "soft", and then the plurality of words are judged to determine that the first information "soft". The classification model then further performs a semantic analysis on the first information to determine a consumer's rating for the product, e.g., if the first information is "very soft", determining the rating as a first rating; determining that the evaluation level is a second level in the case that the first information is "very soft"; in the case that the first information is "soft", it is determined that the evaluation level is a third level, wherein different evaluation levels correspond to different scores.
Optionally, in the analysis mode based on the depth model, the e-commerce platform firstly acquires the vector corresponding to the second information, performs splicing processing on the attribute word and the vector corresponding to the second information to obtain third information, and then processes the third information based on the first neural network model to obtain emotion data. The second information is information except for attribute words in at least one piece of comment data.
It should be noted that the first neural network model is the depth model, where the first neural network model may be, but is not limited to, a CNN (Convolutional Neural Network ) model, an RNN (Recurrent Neural Network, cyclic neural network) model.
The explanation will be given taking the comment of good baby and soft fabric as an example. The attribute word can be determined to be the fabric through the sequence labeling model, after the attribute word is determined, the fabric and other vocabularies (namely second information) in the comment of the fabric is very good and the fabric is very soft are spliced to obtain spliced vocabularies (namely third information), a plurality of pieces of third information can be obtained at the moment, and target third information is selected from the plurality of pieces of third information based on the first neural network model. Similarly, after the target third information is obtained, semantic analysis is performed on the target third information to determine the consumer's rating for the product.
In an alternative scheme, before the emotion data of the product is obtained from at least one piece of comment data based on the attribute words, the electronic commerce platform can also count comment amounts of the at least one piece of comment data of the product in a product transaction process, and determine whether to adopt an analysis mode based on a classification model or an analysis mode based on a depth model according to the comment amounts to carry out emotion analysis on the comment data. Optionally, under the condition that the comment quantity is smaller than the preset comment quantity, determining to perform emotion analysis on the comment data by adopting an analysis mode based on a classification model; and under the condition that the comment quantity is larger than or equal to the preset comment quantity, determining to carry out emotion analysis on comment data by adopting an analysis mode based on a depth model.
In an alternative scheme, the e-commerce platform processes at least one piece of comment data based on the second neural network model to obtain a preset matrix, and then determines emotion data based on the preset matrix. Wherein, the elements in the preset matrix at least comprise: mood data and attributes of the product. Alternatively, the second neural network model may be a double-layer architecture model of LSTM in combination with CNN. In addition, the preset matrix is a 3*5 matrix, wherein the rows of the preset matrix respectively represent service attitudes, logistics speeds and product quality of merchants, and the columns of the preset matrix respectively represent emotion of consumers.
It should be noted that, in the process of determining the emotion data based on the preset matrix, the reason for generating the emotion data may also be determined based on the preset matrix, for example, the emotion of the consumer determining the logistics speed is positive emotion through the preset matrix, and the corresponding emotion is happy, so that the consumer may be satisfied with the logistics speed of the product transaction. For another example, the consumer may determine that the emotion of the product quality is negative emotion and the corresponding emotion is anger by presetting the matrix, and may determine that the consumer is not satisfied with the product quality of the product transaction.
Further, as shown in the determining flowchart of satisfaction data in fig. 4, after emotion data and emotion data are obtained, the e-commerce platform further determines satisfaction data of the product based on the emotion data and the emotion data. Specifically, electricity Shang Ping determines first emotion data corresponding to quality data from emotion data, and determines product quality satisfaction according to the first emotion data and the quality data; determining second emotion data corresponding to the logistics data from the emotion data, and determining logistics satisfaction according to the second emotion data and the logistics data; third emotion data corresponding to the service data is determined from the emotion data, and service attitude satisfaction is determined according to the third emotion data and the service data. And then, carrying out weighted average on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction to obtain the product satisfaction, and finally sequencing the product satisfaction according to the time sequence to obtain the product satisfaction data.
Taking product quality satisfaction as an example, wherein the product quality satisfaction satisfies the following formula:
product quality satisfaction = a + (number of positive emotions in quality data-number of negative emotions in quality data) +b + (number of positive emotions in first emotion data-number of negative emotions in first emotion data)
In the above formula, a and b are weight values of emotion data and emotion data, respectively. The electronic commerce platform generalizes the quality data, classifies the data representing the front emotion of the consumer on the product in the quality data, and obtains the front emotion quantity; and classifying the data representing the negative emotion of the consumer to the product in the quality data to obtain the number of negative emotions. Also, the determination method of the number of positive emotions in the first emotion data and the number of negative emotions in the first emotion data is similar to that described above, and will not be described again.
It should be noted that, the satisfaction degree of the physical distribution and the satisfaction degree of the service attitude can be obtained by adopting the method for calculating the satisfaction degree of the product quality, and then the satisfaction degree of the product can be obtained by weighted average of the three satisfaction degrees. In addition, the weight values of the three satisfaction degrees in the process of carrying out weighted average on the three satisfaction degrees can be adjusted according to different product categories, for example, the raw and fresh categories have higher requirements on the logistics speed, and therefore, the weight values of the logistics satisfaction degrees of the raw and fresh categories are larger; the household appliances have higher requirements on the product quality, so the weight value of the product quality satisfaction degree of the household appliances is larger.
Optionally, fig. 5 shows emotion curves based on emotion data, and as can be seen from fig. 5, in a transaction of a month, the positive emotion of a consumer is continuously improved, the negative emotion is continuously reduced, and the problem of a merchant in continuously improving the transaction process is illustrated, so that the overall improvement of the satisfaction degree of consumption is obvious.
Alternatively, fig. 6 shows satisfaction curves based on emotion data, and as can be seen from fig. 6, consumer feedback commodity quality satisfaction is continuously improved, merchant state satisfaction is kept stable, and satisfaction of logistics speed is continuously reduced. It can be seen that, through the product form, the direction of improving service of the merchant can be effectively guided, as in fig. 6, the merchant needs to follow up the reason that the recent feedback logistics speed is slower, and corresponding improvement measures, such as whether to replace the improvement measures of the logistics provider, are formulated.
In addition, the e-commerce platform can only display the satisfaction data of the product, namely only display the overall satisfaction of the consumer in the product transaction process, and also display the emotion degree change trend of the consumer, or display the change trend of the consumer on the logistics service, the product quality and the service attitude of the product. Fig. 7 is a schematic diagram showing the trend of the emotion degree of the consumer.
From the foregoing, it is apparent that the present application proposes a method for dynamically modeling transaction satisfaction based on user ratings. The satisfaction data can be calculated by related data of three dimensions (namely logistics service, product quality and service attitude). The data of each dimension can be calculated through the difference value between the positive data and the negative data, the whole is dynamically modeled through a satisfaction curve mode, the change trend of the consumer transaction satisfaction is reflected, and whether the business is fed back to improve the service quality or not is judged. Further, through the satisfaction curve, the cracking caused by single analysis in the prior art can be avoided, and the satisfaction of consumers can be integrally grasped from the time dimension. In addition, the satisfaction data can also integrally feed back whether the merchant performs service improvement, thereby being beneficial to platform supervision and being beneficial to the direction of merchant focusing improvement.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
From the above description of the embodiments, it will be clear to those skilled in the art that the method for evaluating user satisfaction according to the above embodiments may be implemented by means of software plus a necessary general hardware platform, or may be implemented by hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method described in the embodiments of the present application.
Example 2
According to an embodiment of the present application, there is further provided a method for evaluating user satisfaction, as shown in fig. 8, including the following steps:
step S802, displaying at least one piece of comment data aiming at a product in the product transaction process, wherein the at least one piece of comment data at least comprises emotion data and emotion data, the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the product is evaluated;
Step S804, satisfaction data for the product is displayed, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product, and the satisfaction is determined according to the emotion data and the emotion data.
It should be noted that, the emotion data of the consumer for the product includes three aspects of data, namely positive emotion data, neutral emotion data and negative emotion data, where the emotion data of each aspect may further include a plurality of levels, and each level corresponds to a different emotion score. Optionally, in the present application, the emotion data of the consumer for the product may be obtained from at least one piece of comment data by using a sequence labeling manner, and the emotion data of the consumer for the product may be obtained from at least one piece of comment data by using a neural network model.
In addition, satisfaction in trading a product includes at least: product quality satisfaction, logistics satisfaction and service attitude satisfaction, and emotion data at least comprise: the service data, the logistics data and the quality data of the products of the objects are preset. The preset object is a merchant, service data of the preset object reflects service attitudes of the merchant, logistics data reflects logistics speed, and quality data of products reflects product quality of products purchased by consumers.
Optionally, after the emotion data and the emotion data, the e-commerce platform can calculate and obtain product quality satisfaction according to quality data and corresponding emotion data of the product, calculate and obtain logistics satisfaction according to logistics data and corresponding emotion data, calculate and obtain service attitude satisfaction according to service data of a preset object and corresponding emotion data, then perform weighted average on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction, so as to obtain satisfaction in the process of trading the product, and then display the satisfaction in the time dimension, so as to obtain the change trend of the satisfaction in the process of trading the product. The display form of the satisfaction data can be, but is not limited to, a table and a graph.
In addition, the satisfaction data of the product reflects the satisfaction degree of the consumer on the product transaction, and also reflects the service quality trend of the merchant. For merchants with continuously improved and improved quality of service, the e-commerce platform can be affirmed and supported. For merchants with continuously reduced service quality, the e-commerce platform can follow up and process in time.
From the above, the method and the device comprehensively consider the emotion data and the emotion data influencing the shopping experience of the consumer in the product transaction process, and can obtain the accurate satisfaction degree of the consumer on the product transaction by comprehensively analyzing the emotion data and the emotion data. In addition, after the satisfaction degree of the consumer on the product transaction is obtained, the change trend of the satisfaction degree of the product transaction along with time is further determined. Compared with the prior art, the change trend of the satisfaction degree along with time reflects the service improvement of the merchant, and the purpose of dynamically analyzing the product is achieved, so that the technical effect of dynamically reflecting the service improvement trend of the merchant is achieved, and the technical problem that the conventional method for statically analyzing the product based on historical data cannot dynamically reflect the service improvement change and trend of the merchant is solved.
It should be noted that, the method for evaluating user satisfaction in embodiment 2 is described in embodiment 1, and will not be described here again.
Example 3
According to an embodiment of the present application, there is further provided a data display platform for implementing the above method for evaluating user satisfaction, as shown in fig. 9, where the data display platform includes: a processing unit 901 and a display unit 903.
The processing unit 901 is used for obtaining emotion data and emotion data for a product from at least one piece of comment data in the product transaction process, and then determining satisfaction data of the product based on the emotion data and the emotion data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, the emotion data is used for representing the emotion grade generated when the product is evaluated, and the satisfaction data is used for representing the change trend of satisfaction in the transaction process; a display unit 903 for displaying satisfaction data.
From the above, the method and the device comprehensively consider the emotion data and the emotion data influencing the shopping experience of the consumer in the product transaction process, and can obtain the accurate satisfaction degree of the consumer on the product transaction by comprehensively analyzing the emotion data and the emotion data. In addition, after the satisfaction degree of the consumer on the product transaction is obtained, the change trend of the satisfaction degree of the product transaction along with time is further determined. Compared with the prior art, the change trend of the satisfaction degree along with time reflects the service improvement of the merchant, and the purpose of dynamically analyzing the product is achieved, so that the technical effect of dynamically reflecting the service improvement trend of the merchant is achieved, and the technical problem that the conventional method for statically analyzing the product based on historical data cannot dynamically reflect the service improvement change and trend of the merchant is solved.
It should be noted that, the data display platform in this embodiment corresponds to the e-commerce platform in embodiment 1, and the data display platform in this embodiment may execute the method for evaluating user satisfaction in embodiment 1, and the related content is described in embodiment 1 and is not described herein.
Example 4
According to an embodiment of the present application, there is further provided an apparatus for evaluating user satisfaction for implementing the method for evaluating user satisfaction, as shown in fig. 10, where the apparatus 100 includes: an acquisition module 1001, a first determination module 1003, and a second determination module 1005.
The obtaining module 1001 is configured to obtain at least one comment data for a product in a product transaction process; a first determining module 1003, configured to obtain emotion data and emotion data for a product from at least one piece of comment data, where the emotion data is used for characterizing an evaluation level of attribute information of the evaluated product, and the emotion data is used for characterizing an emotion level generated when the evaluated product is evaluated; a second determining module 1005 is configured to determine satisfaction data of the product based on the emotion data and the emotion data, where the satisfaction data is used to characterize a trend of change in satisfaction during the transaction of the product.
Here, it should be noted that the above-mentioned obtaining module 1001, the first determining module 1003, and the second determining module 1005 correspond to steps S302 to S306 in embodiment 1, and the three modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in the above-mentioned embodiment one. It should be noted that the above-described module may be operated as a part of the apparatus in the computer terminal 10 provided in the first embodiment.
In an alternative solution, the obtaining module includes: the first acquisition module and the second acquisition module. The first obtaining module is configured to extract an attribute word of a product from at least one comment data of the product, where the attribute word characterizes an attribute of the product, and the attribute at least includes: presetting the service attitude, the logistics speed and the product quality of an object; the second obtaining module is used for obtaining emotion data for the product from at least one piece of comment data based on the attribute word, wherein the emotion data at least comprises: the service data, the logistics data and the quality data of the products of the objects are preset.
In an alternative solution, the first acquisition module includes: and a first processing module. The first processing module is used for processing at least one piece of comment data based on the sequence labeling model to obtain attribute words, wherein the sequence labeling model is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises the attribute words and labels corresponding to the attribute words.
In an alternative, the second obtaining module includes: and a third determining module and a second processing module. The third determining module is used for determining first information from at least one piece of comment data, wherein the first information is information associated with an attribute word in the at least one piece of comment data; the second processing module is used for processing the first information based on a classification model to obtain an evaluation grade aiming at the product and obtain emotion data, wherein the classification model is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises the first information and a label corresponding to the evaluation grade.
In an alternative, the second obtaining module includes: the device comprises a third acquisition module, a third processing module and a fourth processing module. The third acquisition module is used for acquiring a vector corresponding to second information, wherein the second information is information except for an attribute word in at least one piece of comment data; the third processing module is used for performing splicing processing on the attribute words and vectors corresponding to the second information to obtain third information; and the fourth processing module is used for processing the third information based on the first neural network model to obtain emotion data.
In an alternative, the first determining module includes: a fifth processing module and a fourth determining module. The fifth processing module is configured to process at least one piece of comment data based on the second neural network model to obtain a preset matrix, where elements in the preset matrix at least include: mood data and attributes of the product; and a fourth determining module, configured to determine emotion data based on the preset matrix.
In an alternative, the satisfaction comprises at least: product quality satisfaction, logistics satisfaction and service attitude satisfaction, wherein the second determining module comprises: the system comprises a fifth determining module, a sixth determining module, a seventh determining module, a sixth processing module and a sequencing module. The fifth determining module is used for determining first emotion data corresponding to the quality data from the emotion data and determining product quality satisfaction according to the first emotion data and the quality data; a sixth determining module, configured to determine second emotion data corresponding to the logistic data from the emotion data, and determine the logistic satisfaction according to the second emotion data and the logistic data; a seventh determining module, configured to determine third emotion data corresponding to the service data from the emotion data, and determine service attitude satisfaction according to the third emotion data and the service data; the sixth processing module is used for carrying out weighted average on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction to obtain the product satisfaction; and the ordering module is used for ordering the satisfaction degree of the products according to the time sequence to obtain the satisfaction degree data of the products.
Example 5
According to an embodiment of the present application, there is also provided a system for evaluating user satisfaction, where the system includes: a processor and a memory.
The memory is connected with the processor and is used for providing instructions for the processor to process the following processing steps: acquiring at least one comment data aiming at a product in the process of product transaction; acquiring emotion data and emotion data aiming at a product from at least one piece of comment data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the evaluated product is evaluated; and determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product.
In the method, the satisfaction degree data of the product is adopted to determine the change trend of the satisfaction degree in the process of trading the product, after at least one comment data for the product is obtained in the process of trading the product, emotion data and emotion data for the product are obtained from the at least one comment data, and then the change trend of the satisfaction degree in the process of trading the product is determined based on the emotion data and the emotion data.
It is easy to notice that, this application has taken into consideration emotion data and emotion data that influence consumer's shopping experience in the product transaction process comprehensively, through carrying out comprehensive analysis to emotion data and emotion data, can obtain accurate consumer's satisfaction to the product transaction. In addition, after the satisfaction degree of the consumer on the product transaction is obtained, the change trend of the satisfaction degree of the product transaction along with time is further determined. Compared with the prior art, the change trend of the satisfaction degree along with time reflects the service improvement of the merchant, and the purpose of dynamically analyzing the product is achieved, so that the technical effect of dynamically reflecting the service improvement trend of the merchant is achieved, and the technical problem that the conventional method for statically analyzing the product based on historical data cannot dynamically reflect the service improvement change and trend of the merchant is solved.
It should be noted that, the processor may execute the method for evaluating user satisfaction provided in embodiment 1, and the related content is the same as that described in embodiment 1, and will not be described herein.
Example 6
Embodiments of the present application may provide a computer device, which may be any one of a group of computer terminals. Alternatively, in the present embodiment, the above-mentioned computer device may be replaced with a terminal device such as a mobile terminal.
Alternatively, in this embodiment, the above-mentioned computer device may be located in at least one network device among a plurality of network devices of the computer network.
In this embodiment, the computer device may execute the program code of the following steps in the method for evaluating user satisfaction: acquiring at least one comment data aiming at a product in the process of product transaction; acquiring emotion data and emotion data aiming at a product from at least one piece of comment data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the evaluated product is evaluated; and determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product.
Alternatively, FIG. 11 is a block diagram of a computer device according to an embodiment of the present application. As shown in fig. 11, the computer device 110 may include: one or more (only one is shown) processors 1102, a memory 1104, and a transmission 1106.
The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the user satisfaction evaluation method and apparatus in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the user satisfaction evaluation method described above. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located with respect to the processor, which may be connected to the device 110 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may call the information and the application program stored in the memory through the transmission device to perform the following steps: acquiring at least one comment data aiming at a product in the process of product transaction; acquiring emotion data and emotion data aiming at a product from at least one piece of comment data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the evaluated product is evaluated; and determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product.
Optionally, the above processor may further execute program code for: extracting attribute words of the product from at least one comment data of the product, wherein the attribute words characterize the attribute of the product, and the attribute at least comprises: presetting the service attitude, the logistics speed and the product quality of an object; acquiring emotion data for the product from at least one piece of comment data based on the attribute word, wherein the emotion data at least comprises: the service data, the logistics data and the quality data of the products of the objects are preset.
Optionally, the above processor may further execute program code for: and processing at least one piece of comment data based on a sequence labeling model to obtain an attribute word, wherein the sequence labeling model is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises the attribute word and a label corresponding to the attribute word.
Optionally, the above processor may further execute program code for: determining first information from at least one piece of comment data, wherein the first information is information associated with an attribute word in the at least one piece of comment data; and processing the first information based on a classification model to obtain an evaluation grade aiming at the product and obtain emotion data, wherein the classification model is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises the first information and a label corresponding to the evaluation grade.
Optionally, the above processor may further execute program code for: obtaining a vector corresponding to second information, wherein the second information is information except for attribute words in at least one piece of comment data; splicing the attribute words and vectors corresponding to the second information to obtain third information; and processing the third information based on the first neural network model to obtain emotion data.
Optionally, the above processor may further execute program code for: processing at least one piece of comment data based on the second neural network model to obtain a preset matrix, wherein elements in the preset matrix at least comprise: mood data and attributes of the product; mood data is determined based on the preset matrix.
Optionally, the above processor may further execute program code for: determining first emotion data corresponding to the quality data from the emotion data, and determining product quality satisfaction according to the first emotion data and the quality data; determining second emotion data corresponding to the logistics data from the emotion data, and determining logistics satisfaction according to the second emotion data and the logistics data; determining third emotion data corresponding to the service data from the emotion data, and determining service attitude satisfaction according to the third emotion data and the service data; carrying out weighted average on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction to obtain the product satisfaction; and ordering the satisfaction degree of the products according to the time sequence to obtain the satisfaction degree data of the products.
It will be appreciated by those skilled in the art that the configuration shown in fig. 11 is merely illustrative, and the computer device may be a terminal device such as a smart phone (e.g. an Android phone, an iOS phone, etc.), a tablet computer, a palm-phone computer, and a mobile internet device (Mobile Internet Devices, MID), a PAD, etc. Fig. 11 is not limited to the structure of the electronic device. For example, the computer device 110 may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 11, or have a different configuration than shown in FIG. 11.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute in association with hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.
Example 7
Embodiments of the present application also provide a storage medium. Alternatively, in this embodiment, the storage medium may be used to store program code executed by the user satisfaction evaluating method provided in the first embodiment.
Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: acquiring at least one comment data aiming at a product in the process of product transaction; acquiring emotion data and emotion data aiming at a product from at least one piece of comment data, wherein the emotion data is used for representing the evaluation grade of attribute information of the evaluated product, and the emotion data is used for representing the emotion grade generated when the evaluated product is evaluated; and determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of the satisfaction in the process of trading the product.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: extracting attribute words of the product from at least one comment data of the product, wherein the attribute words characterize the attribute of the product, and the attribute at least comprises: presetting the service attitude, the logistics speed and the product quality of an object; acquiring emotion data for the product from at least one piece of comment data based on the attribute word, wherein the emotion data at least comprises: the service data, the logistics data and the quality data of the products of the objects are preset.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: and processing at least one piece of comment data based on a sequence labeling model to obtain an attribute word, wherein the sequence labeling model is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises the attribute word and a label corresponding to the attribute word.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: determining first information from at least one piece of comment data, wherein the first information is information associated with an attribute word in the at least one piece of comment data; and processing the first information based on a classification model to obtain an evaluation grade aiming at the product and obtain emotion data, wherein the classification model is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises the first information and a label corresponding to the evaluation grade.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: obtaining a vector corresponding to second information, wherein the second information is information except for attribute words in at least one piece of comment data; splicing the attribute words and vectors corresponding to the second information to obtain third information; and processing the third information based on the first neural network model to obtain emotion data.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: processing at least one piece of comment data based on the second neural network model to obtain a preset matrix, wherein elements in the preset matrix at least comprise: mood data and attributes of the product; mood data is determined based on the preset matrix.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: determining first emotion data corresponding to the quality data from the emotion data, and determining product quality satisfaction according to the first emotion data and the quality data; determining second emotion data corresponding to the logistics data from the emotion data, and determining logistics satisfaction according to the second emotion data and the logistics data; determining third emotion data corresponding to the service data from the emotion data, and determining service attitude satisfaction according to the third emotion data and the service data; carrying out weighted average on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction to obtain the product satisfaction; and ordering the satisfaction degree of the products according to the time sequence to obtain the satisfaction degree data of the products.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.

Claims (12)

1. The method for evaluating the user satisfaction is characterized by comprising the following steps:
acquiring at least one piece of comment data aiming at a product in the process of product transaction;
acquiring emotion data and emotion data for the product from the at least one piece of comment data, wherein the emotion data is used for representing an evaluation grade of attribute information for evaluating the product, and the emotion data is used for representing an emotion grade generated when evaluating the product;
determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing the change trend of satisfaction in the process of trading the product;
wherein, the satisfaction at least comprises: product quality satisfaction, logistic satisfaction, and service attitude satisfaction, wherein determining satisfaction data for the product based on the emotion data and the emotion data comprises:
Determining first emotion data corresponding to the quality data from the emotion data, and determining the product quality satisfaction according to the first emotion data and the quality data;
determining second emotion data corresponding to the logistics data from the emotion data, and determining the logistics satisfaction according to the second emotion data and the logistics data;
determining third emotion data corresponding to the service data from the emotion data, and determining the service attitude satisfaction according to the third emotion data and the service data;
weighted average is carried out on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction, so that the product satisfaction is obtained;
and sequencing the satisfaction degree of the products according to the time sequence to obtain the satisfaction degree data of the products.
2. The method of claim 1, wherein obtaining emotion data for the product from the at least one piece of comment data comprises:
extracting an attribute word of the product from at least one piece of comment data of the product, wherein the attribute word characterizes the attribute of the product, and the attribute at least comprises: presetting the service attitude, the logistics speed and the product quality of an object;
Acquiring emotion data for the product from the at least one piece of comment data based on the attribute word, wherein the emotion data at least comprises: service data, logistics data of the preset object and quality data of the product.
3. The method of claim 2, wherein extracting the attribute word of the product from at least one piece of comment data of the product comprises:
and processing the at least one comment data based on a sequence labeling model to obtain the attribute word, wherein the sequence labeling model is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises the attribute word and a label corresponding to the attribute word.
4. The method of claim 3, wherein obtaining emotion data for the product from the at least one piece of comment data based on the attribute word comprises:
determining first information from the at least one piece of comment data, wherein the first information is information associated with the attribute word in the at least one piece of comment data;
and processing the first information based on a classification model to obtain an evaluation grade aiming at the product, and obtaining the emotion data, wherein the classification model is trained by using a plurality of groups of data through machine learning, and each group of data in the plurality of groups of data comprises the first information and a label corresponding to the evaluation grade.
5. The method of claim 3, wherein obtaining emotion data for the product from the at least one piece of comment data based on the attribute word comprises:
obtaining a vector corresponding to second information, wherein the second information is information except the attribute words in the at least one piece of comment data;
splicing the attribute words and vectors corresponding to the second information to obtain third information;
and processing the third information based on the first neural network model to obtain the emotion data.
6. The method of claim 1, wherein obtaining mood data for the product from the at least one piece of comment data comprises:
processing the at least one piece of comment data based on a second neural network model to obtain a preset matrix, wherein elements in the preset matrix at least comprise: the mood data and the attributes of the product;
and determining the emotion data based on the preset matrix.
7. The method for evaluating the user satisfaction is characterized by comprising the following steps:
displaying at least one piece of comment data aiming at a product in the process of product transaction, wherein the at least one piece of comment data at least comprises emotion data and emotion data, the emotion data is used for representing an evaluation grade of attribute information for evaluating the product, and the emotion data is used for representing an emotion grade generated when evaluating the product;
Displaying satisfaction data for the product, wherein the satisfaction data is used for representing a change trend of satisfaction in the process of trading the product, the satisfaction is determined according to the emotion data and the emotion data, and the satisfaction at least comprises: product quality satisfaction, logistic satisfaction, and service attitude satisfaction, wherein determining satisfaction data for the product based on the emotion data and the emotion data comprises: determining first emotion data corresponding to the quality data from the emotion data, and determining the product quality satisfaction according to the first emotion data and the quality data; determining second emotion data corresponding to the logistics data from the emotion data, and determining the logistics satisfaction according to the second emotion data and the logistics data; determining third emotion data corresponding to the service data from the emotion data, and determining the service attitude satisfaction according to the third emotion data and the service data; weighted average is carried out on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction, so that the product satisfaction is obtained; and sequencing the satisfaction degree of the products according to the time sequence to obtain the satisfaction degree data of the products.
8. A data display platform, comprising:
the processing unit is used for obtaining emotion data and emotion data for the product from at least one piece of comment data in the process of product transaction, and then determining satisfaction data of the product based on the emotion data and the emotion data, wherein the emotion data is used for representing an evaluation grade of attribute information for evaluating the product, the emotion data is used for representing an emotion grade generated when evaluating the product, and the satisfaction data is used for representing a change trend of satisfaction in the process of transaction of the product, and the satisfaction data at least comprises: product quality satisfaction, logistic satisfaction, and service attitude satisfaction, wherein determining satisfaction data for the product based on the emotion data and the emotion data comprises: determining first emotion data corresponding to the quality data from the emotion data, and determining the product quality satisfaction according to the first emotion data and the quality data; determining second emotion data corresponding to the logistics data from the emotion data, and determining the logistics satisfaction according to the second emotion data and the logistics data; determining third emotion data corresponding to the service data from the emotion data, and determining the service attitude satisfaction according to the third emotion data and the service data; weighted average is carried out on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction, so that the product satisfaction is obtained; ordering the satisfaction of the products according to the time sequence to obtain satisfaction data of the products;
And the display unit is used for displaying the satisfaction data.
9. An evaluation device for user satisfaction, comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring at least one piece of comment data aiming at a product in the process of product transaction;
a first determining module, configured to obtain emotion data and emotion data for the product from the at least one piece of comment data, where the emotion data is used for characterizing an evaluation level of attribute information for evaluating the product, and the emotion data is used for characterizing an emotion level generated when evaluating the product;
the second determining module is used for determining satisfaction degree data of the product based on the emotion data and the emotion data, wherein the satisfaction degree data is used for representing the change trend of the satisfaction degree in the process of trading the product; the satisfaction comprises at least: product quality satisfaction, logistics satisfaction, and service attitude satisfaction, wherein the second determining module further comprises: the system comprises a fifth determining module, a sixth determining module, a seventh determining module, a sixth processing module and a sequencing module. The fifth determining module is used for determining first emotion data corresponding to the quality data from the emotion data and determining product quality satisfaction according to the first emotion data and the quality data; a sixth determining module, configured to determine second emotion data corresponding to the logistic data from the emotion data, and determine the logistic satisfaction according to the second emotion data and the logistic data; a seventh determining module, configured to determine third emotion data corresponding to the service data from the emotion data, and determine service attitude satisfaction according to the third emotion data and the service data; the sixth processing module is used for carrying out weighted average on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction to obtain the product satisfaction; and the ordering module is used for ordering the satisfaction degree of the products according to the time sequence to obtain the satisfaction degree data of the products.
10. A storage medium comprising a stored program, wherein the program, when run, controls a device in which the storage medium is located to perform the method of evaluating user satisfaction of any one of claims 1 to 6.
11. A computer device comprising a processor, wherein the processor is configured to run a program, the program running to perform the method of evaluating user satisfaction of any of claims 1 to 6.
12. An evaluation system for user satisfaction, comprising:
a processor; and
a memory, coupled to the processor, for providing instructions to the processor to process the following processing steps:
acquiring at least one piece of comment data aiming at a product in the process of product transaction;
acquiring emotion data and emotion data for the product from the at least one piece of comment data, wherein the emotion data is used for representing an evaluation grade of attribute information for evaluating the product, and the emotion data is used for representing an emotion grade generated when evaluating the product;
determining satisfaction data of the product based on the emotion data and the emotion data, wherein the satisfaction data is used for representing a change trend of satisfaction in the process of trading the product, and the satisfaction at least comprises: product quality satisfaction, logistic satisfaction, and service attitude satisfaction, wherein determining satisfaction data for the product based on the emotion data and the emotion data comprises: determining first emotion data corresponding to the quality data from the emotion data, and determining the product quality satisfaction according to the first emotion data and the quality data; determining second emotion data corresponding to the logistics data from the emotion data, and determining the logistics satisfaction according to the second emotion data and the logistics data; determining third emotion data corresponding to the service data from the emotion data, and determining the service attitude satisfaction according to the third emotion data and the service data; weighted average is carried out on the product quality satisfaction, the logistics satisfaction and the service attitude satisfaction, so that the product satisfaction is obtained; and sequencing the satisfaction degree of the products according to the time sequence to obtain the satisfaction degree data of the products.
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