CN110888983B - Positive and negative emotion analysis method, terminal equipment and storage medium - Google Patents
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Abstract
The invention relates to a positive and negative emotion analysis method, terminal equipment and a storage medium, wherein the method comprises the following steps: s1: establishing and maintaining industry keyword rules and industry emotion dictionaries corresponding to different industries; s2: judging whether the text data to be analyzed contains keywords contained in the industry keyword rule, and if yes, entering S3; otherwise, go to S4; s3: judging all industries to which the text data belongs according to the industries to which the keywords belong and the industry keyword rules corresponding to the industries, and then calculating the emotion score of each industry according to the industry emotion dictionary corresponding to each industry so as to obtain the emotion positive and negative analysis result of the text data; s4: and obtaining the positive and negative emotion analysis result of the text data through the trained machine learning model. The invention adopts an emotion analysis method based on the fusion of industry emotion words and a machine learning model, divides and treats the network texts, and improves the analysis effect.
Description
Technical Field
The invention relates to the technical field of text analysis, in particular to a positive and negative emotion analysis method, terminal equipment and a storage medium.
Background
Explosive growth of network data places more and higher demands on the analysis of data. The text analysis and mining technology is a technology widely applied at present, semantic contents of texts are extracted through corresponding technologies and methods, and then a series of operations such as classification, clustering, positive and negative emotion analysis and the like are performed on the texts, and the method is mainly used for the fields of commodity recommendation, public opinion analysis, text search and the like.
In public opinion analysis, public opinions in a network need to be sorted and analyzed under different topics, for example, emotion positive and negative analysis is performed on collected texts, emotion positive and negative trends of postings are automatically recognized, enterprises and governments can better grasp development conditions of the network public opinions, and a foundation is provided for public opinion guidance later. Therefore, the emotion positive and negative analysis of the collected text is a relatively important link in public opinion analysis.
In the related technology of text classification, the current emotion analysis is mainly divided into two methods, namely dictionary-based and machine learning-based. The method based on the emotion dictionary mainly needs a reliable knowledge base such as the emotion dictionary, and the method based on the machine learning needs a large amount of labeled samples. The text data to be analyzed in the public opinion field is very complicated, and can be generally divided into general fields and industrial fields according to business requirements. In the general field, the high-quality general field emotion dictionaries are very few, and in addition, semantic information is not fully considered based on the rule-based algorithm characteristics of the emotion dictionaries, so that the effect of using the algorithm based on the emotion dictionaries in the general field is not ideal. The algorithm based on machine learning, particularly the deep learning, is started in recent years, semantic information can be well combined, and in addition, the general field has a plurality of open source samples or open source models with good quality, so that the emotion analysis in the general field can be well represented based on the deep learning algorithm. However, in the field of special industries, manual collection of high-quality emotion knowledge bases becomes possible, emotion analysis in the special industries has relatively little dependence on semantics, good effects can be obtained through the emotion knowledge bases, and collection of a large number of industry label samples is time-consuming and labor-consuming.
Disclosure of Invention
Based on the problems, the invention provides a positive and negative emotion analysis method, terminal equipment and a storage medium, wherein a network text is treated in a separate manner by adopting an emotion analysis method based on the fusion of industry emotion words and a machine learning model, industry field data (such as finance and economics and the like) are analyzed through an algorithm based on an industry emotion dictionary, general field data are analyzed through an algorithm of the machine learning model, and the overall analysis effect is improved.
The specific scheme is as follows:
a positive and negative face emotion analysis method comprises the following steps:
s1: establishing and maintaining an industry keyword rule and an industry emotion dictionary corresponding to different industries, wherein the industry emotion dictionary comprises main words capable of expressing the type of the industry and emotion words capable of expressing the emotion of the industry;
s2: judging whether the text data to be analyzed contains keywords contained in the industry keyword rule, and if yes, entering S3; otherwise, go to S4;
s3: judging all industries to which the text data belongs according to the industries to which the keywords belong and industry keyword rules corresponding to the industries, and then calculating emotion scores of each industry according to industry emotion dictionaries corresponding to each industry so as to obtain emotion positive and negative analysis results of the text data;
s4: and obtaining the positive and negative emotion analysis result of the text data through the trained machine learning model.
Further, the method for judging all industries to which the text data belongs in step S3 is as follows: and calculating Boolean operation results of the industry keyword rules corresponding to each industry according to all keywords contained in the text data, and judging whether the industries belong to the industry or not according to the results.
Furthermore, each industry corresponds to an industry keyword rule, when the Boolean operation result of the industry keyword rule is true, the industry is judged to belong to, and when the result is false, the industry is judged not to belong to.
Furthermore, each industry corresponds to a plurality of industry keyword rules, when the Boolean operation result of one industry keyword rule is true, the industry is judged to belong to, and when all the results are false, the industry is judged not to belong to.
Further, the industry keyword rule is a boolean operation among a plurality of keywords.
Further, the emotion score of the text data under each industry is calculated by the following steps:
dividing the text data into a plurality of sentences, identifying emotion words and main words contained in each sentence according to industry emotion dictionaries corresponding to industries to which the text data belong, and calculating emotion Score of each sentencesentence：
Wherein the subscript w denotes the affective word, ScorewExpressing the sentiment score of the sentiment word w recorded in the industry sentiment dictionary, dwRepresenting the distance between the main word and the emotional word w;
score according to emotion Score of each sentencesentenceAnd the text length of the text data, calculating the emotion Score of the text datacontent：
Wherein: len (a)contentIndicating the text length of the text data.
Further, the method for obtaining the positive and negative emotion analysis result of the text data according to the emotion score of the text data comprises the following steps:
when the text data only belongs to one industry, if the emotion score is positive, the text data is positive emotion; when the emotion score is negative, negative emotion is detected; when the emotion score is zero, the emotion score is a middle emotion;
and when the text data belongs to at least two industries, calculating the emotion score under each industry, and judging that the text data belongs to positive emotion, middle emotion or negative emotion according to a voting method.
Further, the machine learning model is a BilSTM deep cycle network model.
The positive and negative emotion analysis terminal device comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to an embodiment of the invention as described above.
According to the invention, by adopting the technical scheme, the network texts are treated in a separate way by an emotion analysis method based on the fusion of the industry emotion words and the machine learning model, and the industry field data (such as finance and economics and the like) is analyzed by an algorithm based on the industry emotion dictionaries, only one set of industry keyword rules and the industry emotion dictionaries need to be maintained, different industries load different emotion dictionaries and introduce main words and word intervals, so that the emotion analysis effect of the industry texts is effectively improved; the general field text adopts a machine learning model algorithm of the current process, and the overall emotion analysis effect is improved.
Drawings
Fig. 1 is a flowchart illustrating a first embodiment of the present invention.
Fig. 2 is a flowchart illustrating the operation of the BiLSTM deep-loop network model according to an embodiment of the present invention.
Detailed Description
To further illustrate the various embodiments, the present invention provides the accompanying figures. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. With these references, one of ordinary skill in the art will appreciate other possible embodiments and advantages of the present invention.
The invention will now be further described with reference to the drawings and the detailed description.
The first embodiment is as follows:
the embodiment of the invention provides a positive and negative emotion analysis method, as shown in FIG. 1, the method comprises the following steps:
s1: and establishing and maintaining industry keyword rules and industry emotion dictionaries corresponding to different industries.
The industry emotion dictionary comprises main words capable of representing the type of the industry, for example, the main words of the police-related industry comprise: police, jc, traffic police, etc., and emotional words that can represent the industry's emotions.
Each industry may correspond to one or more industry keyword rules, each industry keyword rule is boolean operations among a plurality of keywords, and the format of the industry keyword rules set in this embodiment is: the word 1| | word 2| | word 3& & word 4& & &! Word 5, wherein: | l represents a logical OR operation, & & represents a logical AND operation, |! Indicating a logical not operation, and the operation priority can be specified by "()" between logical operators.
S2: judging whether the text data to be analyzed contains keywords contained in the industry keyword rule, and if yes, entering S3; otherwise, the process proceeds to S4.
In this embodiment, whether the text data contains a keyword is determined by an AC automaton algorithm.
S3: and after judging all industries to which the text data belongs according to the industries to which the keywords belong and the industry keyword rules corresponding to the industries, calculating the emotion score of each industry according to the industry emotion dictionary corresponding to each industry, and further obtaining the emotion positive and negative analysis result of the text data.
(1) Industry judgment
The method for judging all industries to which the text data belongs comprises the following steps: and calculating Boolean operation results of the industry keyword rules corresponding to each industry according to all keywords contained in the text data, and judging whether the industries belong to the industry or not according to the results.
In this embodiment, the keywords in the industry keyword rule are replaced with "true" or "false", the keywords included in the text data are set as true, and the keywords not included are set as false, if the text data include word 1, word 3, and word 5, and do not include word 2 and word 4, the rule is rewritten as: true | | | false | | true & & false & & &! And performing Boolean operation on the rewritten result to obtain a final result. In this embodiment, a high-efficiency FEL boolean operation engine is used to perform boolean operations.
And when each industry corresponds to an industry keyword rule, judging that the industry belongs to the industry when the Boolean operation result of the industry keyword rule is true, and judging that the industry does not belong to the industry when the result is false.
And when each industry corresponds to at least two industry keyword rules, if the Boolean operation result of one industry keyword rule is true, judging that the industry belongs to the industry, and if all the results are false, judging that the industry does not belong to the industry.
(2) Sentiment score calculation
The emotion score of the text data under each industry is calculated by the following steps:
A. dividing the text data into a plurality of sentences, identifying emotion words and main words contained in each sentence according to industry emotion dictionaries corresponding to industries to which the text data belong, and calculating emotion Score of each sentencesentence：
Wherein the subscript w denotes the affective word, ScorewExpressing the emotion score of the emotion word w recorded in the industry emotion dictionary, dwAnd representing the distance between the main words and the emotional words w.
The closer the sentiment word is to the subject word in the sentence, the higher the weight. If the long sentence does not contain the main word, the weight of the emotional word is 0 or a value smaller than the weight of the main word, and is preferably set to 0 in this embodiment.
In this embodiment, the sentence is divided into passes. "to divide.
B. Score according to emotion Score of each sentencesentenceAnd the text length of the text data, calculating the emotion Score of the text datacontent：
Wherein: len (a)contentIndicating the text length of the text data.
(3) Determination of positive and negative sensitivity analysis results
When the emotion score is positive, the emotion score is usually positive emotion, and the probability of being positive is higher if the emotion score is larger;
when the emotion score is negative, it is generally negative, and the probability of being positive increases as the emotion score decreases.
Therefore, in this embodiment, a positive emotion threshold and a negative emotion threshold are set, and for each industry, a positive emotion is given when the emotion score of the text data is greater than the positive emotion threshold, a negative emotion is given when the emotion score is less than the negative emotion threshold, and an intermediate emotion is given when the emotion score is between the positive emotion threshold and the negative emotion threshold.
When the text data belong to at least two industries, judging through a voting method, namely when the number of industries with positive emotions of the text data belonging to the industry is larger than that of industries with negative emotions of the text data belonging to the industry, judging as the positive emotions; when the number of industries with positive emotions of the industry to which the text data belong is smaller than that of industries with negative emotions of the industry to which the text data belong, the industries are negative emotions; and when the number of industries with positive emotions of the industry to which the text data belong is equal to the number of industries with negative emotions of the industry, the industry is the middle emotion.
S4: and obtaining the positive and negative emotion analysis result of the text data through the trained machine learning model.
The training is performed through a large amount of text data with labels searched on the Internet.
The machine learning model in this embodiment employs a BilSTM deep cycle network model.
The BilSTM deep circulation network model combines the word order and the deep semantic information of the positive direction and the negative direction of the text, solves the long-term dependence problem through the LSTM gating result, and is a structure which is most widely used in the field of natural language processing at present.
As shown in fig. 2, in this embodiment, word segmentation is performed on the text data, then each word is represented as a dense vector through an embedding layer, then two hidden layer vectors are obtained through positive and negative LSTM network modules, the two hidden layer vectors are concatered, and finally, an emotion positive and negative analysis result of the text data is obtained through a softmax layer.
According to the emotion analysis method based on the fusion of the industry emotion words and the machine learning model, the network texts are treated in a separating way, the industry field data (such as finance and economics) are analyzed through an algorithm based on the industry emotion dictionaries, only one set of industry keyword rules and the industry emotion dictionaries need to be maintained, different industries load different emotion dictionaries and introduce main word and word intervals, and the emotion analysis effect of the industry texts is effectively improved; the general field text adopts the machine learning model algorithm of the current process, and the overall emotion analysis effect is improved based on the algorithm analysis of the BilSTM depth model. The method has the following beneficial effects:
(1) the method has the advantages that the industry keyword rules are introduced, the texts are divided into specific industry categories, the emotion words of specific industries are loaded, the purpose of association analysis of the texts and the industry emotion words is achieved, the emotion analysis accuracy of specific industries is improved, the emotion words and the industry keyword rules of related industries can be conveniently added in the later period according to actual effects, and classification effects are improved.
(2) A BilSTM deep cycle network model is introduced, and the model combines the word order information of the text in the positive direction and the negative direction, so that the long-term dependence problem is effectively solved, and the prediction effect of the model is improved. The method can find related corpus training models on the Internet and initial models in related general fields, and can optimize the training models by using a transfer learning technology on the basis of the original models according to continuous sample accumulation in the later period, so that the model prediction effect is improved.
(3) The method based on the industry emotion words and the base machine learning model is fused, the industry keyword rule is hit and predicted through the emotion dictionary, and other data are predicted through the machine learning model.
The second embodiment:
the invention also provides positive and negative emotion analysis terminal equipment, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the steps in the method embodiment of the first embodiment of the invention are realized when the processor executes the computer program.
Further, as an executable scheme, the positive and negative emotion analysis terminal device may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The positive and negative emotion analysis terminal device can comprise a processor and a memory. It can be understood by those skilled in the art that the above-mentioned positive and negative emotion analysis terminal device is only an example of the positive and negative emotion analysis terminal device, and does not constitute a limitation to the positive and negative emotion analysis terminal device, and may include more or fewer components than the above, or combine some components, or different components, for example, the positive and negative emotion analysis terminal device may further include an input and output device, a network access device, a bus, and the like, which is not limited in this embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the positive and negative emotion analysis terminal equipment, and various interfaces and circuits are used for connecting all parts of the positive and negative emotion analysis terminal equipment.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the positive and negative emotion analysis terminal device by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The present invention also provides a computer-readable storage medium, which stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method of an embodiment of the present invention.
The positive and negative emotion analysis terminal device integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM ), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A positive and negative emotion analysis method is characterized by comprising the following steps:
s1: establishing and maintaining an industry keyword rule and an industry emotion dictionary corresponding to different industries, wherein the industry emotion dictionary comprises main words capable of expressing the type of the industry and emotion words capable of expressing the emotion of the industry;
s2: judging whether the text data to be analyzed contains keywords contained in the industry keyword rule, and if yes, entering S3; otherwise, go to S4;
s3: judging all industries to which the text data belongs according to the industries to which the keywords belong and industry keyword rules corresponding to the industries, and then calculating emotion scores of each industry according to industry emotion dictionaries corresponding to each industry so as to obtain emotion positive and negative analysis results of the text data; the emotion score of the text data under each industry is calculated by the following steps:
dividing the text data into a plurality of sentences, identifying emotion words and main words contained in each sentence according to industry emotion dictionaries corresponding to industries to which the text data belong, and calculating emotion Score of each sentencesentence：
Wherein the subscript w represents the sentiment word, ScorewExpressing the emotion score of the emotion word w recorded in the industry emotion dictionary, dwRepresenting the distance between the main words and the emotional words w;
score according to emotion Score of each sentencesentenceAnd the text length of the text data, calculating the emotion Score of the text datacontent：
Wherein: len (a)contentA text length representing the text data;
the method for obtaining the positive and negative emotion analysis result of the text data according to the emotion score of the text data comprises the following steps: when the text data only belongs to one industry, judging that the text data belongs to positive emotion, middle emotion or negative emotion according to the size relation between the emotion score and a set threshold value; when the text data belongs to at least two industries, calculating the emotion score under each industry, and judging whether the text data belongs to positive emotion, middle emotion or negative emotion according to a voting method;
s4: and obtaining the positive and negative emotion analysis result of the text data through the trained machine learning model.
2. The positive-negative emotion analysis method according to claim 1, characterized in that: the method for judging all industries to which the text data belongs comprises the following steps: and calculating Boolean operation results of the industry keyword rules corresponding to each industry according to all keywords contained in the text data, and judging whether the industry belongs to the industry or not according to the results.
3. The positive-negative emotion analysis method according to claim 2, characterized in that: each industry corresponds to an industry keyword rule, when the Boolean operation result of the industry keyword rule is true, the industry is judged to belong to, and when the result is false, the industry is judged not to belong to.
4. The positive-negative emotion analysis method according to claim 2, characterized in that: each industry corresponds to at least two industry keyword rules, when the Boolean operation result of one industry keyword rule is true, the industry is judged to belong to, and when all the results are false, the industry is judged not to belong to.
5. The positive-negative emotion analysis method according to claim 1, characterized in that: the industry keyword rule is a boolean operation among a plurality of keywords.
6. The positive and negative emotion analysis method as claimed in claim 1, wherein: the machine learning model is a BilSTM deep cycle network model.
7. The utility model provides a positive negative emotion analysis terminal equipment which characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, the processor implementing the steps of the method according to any one of claims 1 to 6 when executing the computer program.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107301171A (en) * | 2017-08-18 | 2017-10-27 | 武汉红茶数据技术有限公司 | A kind of text emotion analysis method and system learnt based on sentiment dictionary |
| CN108170685A (en) * | 2018-01-29 | 2018-06-15 | 浙江省公众信息产业有限公司 | Text emotion analysis method, device and computer readable storage medium |
| CN109165298A (en) * | 2018-08-15 | 2019-01-08 | 上海文军信息技术有限公司 | A kind of text emotion analysis system of autonomous upgrading and anti-noise |
| CN109858026A (en) * | 2019-01-17 | 2019-06-07 | 深圳壹账通智能科技有限公司 | Text emotion analysis method, device, computer equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10621505B2 (en) * | 2014-04-17 | 2020-04-14 | Hypergrid, Inc. | Cloud computing scoring systems and methods |
-
2019
- 2019-11-26 CN CN201911171315.1A patent/CN110888983B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107301171A (en) * | 2017-08-18 | 2017-10-27 | 武汉红茶数据技术有限公司 | A kind of text emotion analysis method and system learnt based on sentiment dictionary |
| CN108170685A (en) * | 2018-01-29 | 2018-06-15 | 浙江省公众信息产业有限公司 | Text emotion analysis method, device and computer readable storage medium |
| CN109165298A (en) * | 2018-08-15 | 2019-01-08 | 上海文军信息技术有限公司 | A kind of text emotion analysis system of autonomous upgrading and anti-noise |
| CN109858026A (en) * | 2019-01-17 | 2019-06-07 | 深圳壹账通智能科技有限公司 | Text emotion analysis method, device, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于词典和机器学习组合的情感分析;丁蔚;《中国优秀硕士论文全文数据库 信息科技辑》;20170915(第9期);I138-354 * |
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