CN110232181B - Comment analysis method and device - Google Patents
Comment analysis method and device Download PDFInfo
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- CN110232181B CN110232181B CN201810182899.1A CN201810182899A CN110232181B CN 110232181 B CN110232181 B CN 110232181B CN 201810182899 A CN201810182899 A CN 201810182899A CN 110232181 B CN110232181 B CN 110232181B
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- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
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- G06F40/279—Recognition of textual entities
- G06F40/284—Lexical analysis, e.g. tokenisation or collocates
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Abstract
The disclosure relates to a comment analysis method and device. The method comprises the following steps: extracting new words from the comments for the specified object; performing word segmentation processing on the comment according to the new word to obtain a word segmentation result of the comment; performing syntactic analysis on the word segmentation result of the comment to obtain a syntactic analysis result of the comment; determining a viewpoint category corresponding to the comment according to the condition that the syntactic analysis result of the comment contains seed words or new words; and determining the emotional tendency of the specified object in each viewpoint type according to the syntactic analysis result of the comment corresponding to each viewpoint type. The method and the system can perform fine-grained emotional analysis on the specified object, accurately determine the emotional tendency of the specified object in each viewpoint category, help business personnel to know the comment angles and the commendatory and derogatory attitude of the majority of users on the specified object, and fully mine the value of comment information.
Description
Technical Field
The disclosure relates to the technical field of computers, in particular to a comment analysis method and device.
Background
With the continuous popularization of social networks and mobile internet, the cost for people to publish information is lower and lower, and more users are willing to share own opinions and comments on people, events and products on the internet. The comments reflect the opinions and emotional tendencies of people to things and have important significance for public opinion analysis and prediction based on big data. Therefore, emotion analysis techniques have been developed. Emotion analysis is also referred to as view mining, view analysis, and the purpose of emotion analysis is to mine the view of user expression from the text, usually expressed in terms of emotion polarity (e.g., positive, negative, neutral, etc.). The traditional sentiment analysis mainly focuses on the sentiment polarity of the whole comment, but the sentiment polarity of the whole comment is coarse in granularity, and a user cannot judge whether the current product has a good public praise on a certain attribute concerned by the user according to the sentiment polarity of the whole comment. A product with better overall word-of-mouth does not necessarily have good word-of-mouth on every attribute, and there is often some difference in the attributes that different users focus on products of the same category. Therefore, how to perform fine-grained emotion analysis on the product becomes an urgent problem to be solved.
Disclosure of Invention
In view of this, the present disclosure provides a comment analyzing method and apparatus.
According to an aspect of the present disclosure, there is provided a comment analyzing method including:
extracting new words from the comments for the specified object;
performing word segmentation processing on the comment according to the new word to obtain a word segmentation result of the comment;
performing syntactic analysis on the word segmentation result of the comment to obtain a syntactic analysis result of the comment;
determining a viewpoint category corresponding to the comment according to the condition that the syntactic analysis result of the comment contains seed words or new words;
and determining the emotional tendency of the specified object in each viewpoint type according to the syntactic analysis result of the comment corresponding to each viewpoint type.
In a possible implementation manner, determining a viewpoint category corresponding to the comment according to a case that a result of the syntactic analysis of the comment includes a seed word or a new word includes:
and determining the viewpoint category corresponding to the seed word as the viewpoint category corresponding to the comment when the syntactic analysis result of the comment contains the seed word.
In a possible implementation manner, determining a viewpoint category corresponding to the comment according to a case that a result of syntactic analysis of the comment contains a seed word or a new word includes:
determining a seed word matched with the new word under the condition that the syntactic analysis result of the comment contains the new word and does not contain the seed word;
and determining the viewpoint category corresponding to the seed word matched with the new word as the viewpoint category corresponding to the comment.
In one possible implementation, determining a seed word matching the new word includes:
determining a feature vector of the new word according to the probability that each word in the appointed word list and the new word appear in the same comment;
determining a feature vector of the seed word according to the probability that each word in the appointed word list and the seed word appear in the same comment;
and determining the seed word with the highest similarity with the feature vector of the new word as the seed word matched with the new word.
In one possible implementation manner, determining the emotional tendency of the specified object in each viewpoint category according to the result of the syntactic analysis of the comment corresponding to each viewpoint category includes:
for any viewpoint category, converting the syntactic analysis result of the comment corresponding to the viewpoint category into a two-dimensional matrix;
inputting the two-dimensional matrix into a convolutional neural network, and extracting the matching features of all convolutional kernels through the maximum pooling layer of the convolutional neural network;
and determining the emotional tendency of the specified object in the viewpoint category according to the matching features.
In a possible implementation manner, performing syntactic analysis on the word segmentation result of the comment to obtain a syntactic analysis result of the comment includes:
and carrying out syntactic analysis on the participle result of the comment according to the negative word, the name subject, the modifier and the direct object to obtain the syntactic analysis result of the comment.
In a possible implementation manner, determining a viewpoint category corresponding to the comment according to a case that a result of the syntactic analysis of the comment includes a seed word or a new word includes:
and determining the viewpoint type corresponding to the comment according to the condition that the syntactic analysis result of the comment contains the seed word or the new word under the condition that the syntactic analysis result of the comment contains the seed word or the new word and the modifier in the syntactic analysis result of the comment belongs to the specified word bank.
In one possible implementation, the method further includes:
determining typical words of various viewpoint categories;
and taking the typical word and the word with the similarity larger than a first threshold value as seed words.
In one possible implementation, extracting new words from comments directed to a specified object includes:
carrying out adjacent character cutting on the comments aiming at the specified object to obtain a cutting result;
and extracting new words from the comments according to the degree of solidification and the degree of freedom of the cutting result.
According to another aspect of the present disclosure, there is provided a comment analyzing apparatus including:
the extraction module is used for extracting new words from the comments aiming at the specified object;
the word segmentation processing module is used for carrying out word segmentation processing on the comment according to the new word to obtain a word segmentation result of the comment;
the syntactic analysis module is used for carrying out syntactic analysis on the word segmentation result of the comment to obtain a syntactic analysis result of the comment;
the first determining module is used for determining the viewpoint category corresponding to the comment according to the condition that the syntactic analysis result of the comment contains seed words or new words;
and the second determining module is used for determining the emotional tendency of the specified object in each viewpoint type according to the syntactic analysis result of the comment corresponding to each viewpoint type.
In one possible implementation manner, the first determining module includes:
a first determining sub-module, configured to determine, when a syntactic analysis result of the comment includes a seed word, a viewpoint category corresponding to the seed word as a viewpoint category corresponding to the comment.
In one possible implementation manner, the first determining module includes:
a second determining sub-module, configured to determine a seed word matching the new word if the parsing result of the comment includes the new word and does not include the seed word;
and the third determining submodule is used for determining the viewpoint category corresponding to the seed word matched with the new word as the viewpoint category corresponding to the comment.
In one possible implementation, the second determining sub-module includes:
the first determining unit is used for determining a feature vector of the new word according to the probability that each word in the specified word list and the new word appear in the same comment;
the second determining unit is used for determining the feature vector of the seed word according to the probability that each word in the specified word list and the seed word appear in the same comment;
and a third determining unit, configured to determine the seed word with the highest similarity to the feature vector of the new word as the seed word matched with the new word.
In one possible implementation manner, the second determining module includes:
the conversion submodule is used for converting the syntactic analysis result of the comment corresponding to any viewpoint category into a two-dimensional matrix;
the first extraction submodule is used for inputting the two-dimensional matrix into a convolutional neural network and extracting the matching characteristics of all convolutional kernels through the maximum pooling layer of the convolutional neural network;
and the fourth determining submodule is used for determining the emotional tendency of the specified object in the viewpoint category according to the matching features.
In one possible implementation, the syntax analysis module is configured to:
and carrying out syntactic analysis on the participle result of the comment according to the negative word, the name subject, the modifier and the direct object to obtain a syntactic analysis result of the comment.
In one possible implementation manner, the first determining module is configured to:
and determining the viewpoint category corresponding to the comment according to the condition that the syntactic analysis result of the comment contains the seed word or the new word under the condition that the syntactic analysis result of the comment contains the seed word or the new word and the modifier in the syntactic analysis result of the comment belongs to the specified word bank.
In one possible implementation, the apparatus further includes:
the third determining module is used for determining typical words of all viewpoint categories;
and the fourth determining module is used for taking the typical word and the word with the similarity larger than the first threshold as seed words.
In one possible implementation, the extraction module includes:
the cutting submodule is used for cutting adjacent characters of the comments aiming at the specified object to obtain a cutting result;
and the second extraction submodule is used for extracting new words from the comments according to the degree of solidification and the degree of freedom of the cutting result.
According to another aspect of the present disclosure, there is provided a comment analyzing apparatus including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the above method.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.
The comment analysis method and the comment analysis device in each aspect of the disclosure extract new words from comments aiming at a specified object, perform word segmentation processing on the comments according to the new words to obtain word segmentation results of the comments, perform syntactic analysis on the word segmentation results of the comments to obtain syntactic analysis results of the comments, determine viewpoint categories corresponding to the comments according to the condition that the syntactic analysis results of the comments contain seed words or new words, and determine emotional tendencies of the specified object in each viewpoint category according to the syntactic analysis results of the comments corresponding to each viewpoint category, so that fine-grained emotional analysis can be performed on the specified object, the emotional tendencies of the specified object in each viewpoint category can be accurately determined, business personnel can know comment angles and positive and negative attitudes of the specified object of a large number of users, and the value of comment information can be fully mined.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flowchart of a comment analyzing method according to an embodiment of the present disclosure.
Fig. 2 shows an exemplary flowchart of comment analyzing method step S14 according to an embodiment of the present disclosure.
Fig. 3 shows an exemplary flowchart of determining a seed word matching the new word in step S141 of the comment analyzing method according to an embodiment of the present disclosure.
Fig. 4 shows an exemplary flowchart of the comment analyzing method step S15 according to an embodiment of the present disclosure.
Fig. 5 is a schematic diagram illustrating determining emotional tendencies of the designated object in various viewpoint categories in a comment analysis method according to an embodiment of the present disclosure.
Fig. 6a to 6c are schematic diagrams illustrating emotional tendencies of movies or television series videos in various viewpoint categories in a comment analysis method according to an embodiment of the present disclosure.
Fig. 7a and 7b are schematic diagrams illustrating emotional tendencies of a variety video in various viewpoint categories in a comment analysis method according to an embodiment of the present disclosure.
Fig. 8 shows an exemplary flowchart of comment analyzing method step S11 according to an embodiment of the present disclosure.
Fig. 9 shows a block diagram of a comment analyzing apparatus according to an embodiment of the present disclosure.
Fig. 10 illustrates an exemplary block diagram of a comment analyzing apparatus according to an embodiment of the present disclosure.
FIG. 11 is a block diagram illustrating an apparatus 800 for review analysis in accordance with an exemplary embodiment.
FIG. 12 is a block diagram illustrating an apparatus 1900 for review analysis according to an exemplary embodiment.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
Fig. 1 shows a flow diagram of a comment analysis method according to an embodiment of the present disclosure. As shown in fig. 1, the method includes steps S11 through S15.
In step S11, a new word is extracted from the comment for the specified object.
The specified object can refer to any object which needs to be subjected to comment analysis. For example, the designated object may be video, audio, news, a character, an event or product, or the like.
In the present embodiment, a new word extraction technique in the related art may be employed to extract a new word from all comments for a specified object.
In step S12, the comment is participled according to the new word, and a participle result of the comment is obtained.
In one possible implementation manner, the extracted new words may be used as a dictionary of word segmentation, and the comment for the specified object may be subjected to word segmentation. For example, the new words may include actor names, character names, and the like.
In one possible implementation, the comments may be preprocessed before the comments are participled to improve the accuracy and efficiency of comment analysis.
As one example of this implementation, preprocessing the comment may include: the specified character in the comment is deleted. For example, a forward character in a comment such as a microblog may be deleted.
As another example of this implementation, preprocessing the comment may include: and converting traditional characters in the comments into simplified characters.
As another example of this implementation, preprocessing the comment may include: duplicate reviews are deleted.
In step S13, the segmentation result of the comment is parsed, and a parsing result of the comment is obtained.
In this embodiment, a syntactic analyzer in the related art may be adopted to perform syntactic analysis on the word segmentation result of the comment, obtain a syntactic relation between words in each comment, and obtain a syntactic analysis result of the comment. In the embodiment, a syntactic structure capable of expressing a user viewpoint can be extracted by performing syntactic analysis on the segmentation result of the comment.
In a possible implementation manner, performing syntactic analysis on the word segmentation result of the comment to obtain a syntactic analysis result of the comment includes: and carrying out syntactic analysis on the participle result of the comment according to the negative word, the name subject, the modifier and the direct object to obtain a syntactic analysis result of the comment.
As an example of the implementation manner, a segmentation result of the comment may be analyzed in a syntactic manner, a negative word, a name subject, a modifier, and a direct object in the segmentation result may be extracted, and the negative word and the modifier corresponding to the negative word may be merged to obtain a syntactic analysis result of the comment.
In step S14, a point of view category corresponding to the comment is determined based on the fact that the result of the syntactic analysis of the comment includes a seed word or a new word.
For example, a comment "drama is good and special effect is dazzling" of a movie contains the seed words "drama" and "special effect". The viewpoint category corresponding to the seed word "scenario" is "scenario", and the viewpoint category corresponding to the seed word "special effect" is "visual sound effect". It can be determined that the comment category includes "scenario" and "audio-visual effect".
In a possible implementation manner, determining a viewpoint category corresponding to the comment according to a case that a result of the syntactic analysis of the comment includes a seed word or a new word includes: and determining the viewpoint category corresponding to the comment according to the condition that the syntactic analysis result of the comment contains the seed word or the new word under the condition that the syntactic analysis result of the comment contains the seed word or the new word and the modifier in the syntactic analysis result of the comment belongs to the specified word bank. In this implementation, only when the syntax analysis result of the comment includes a seed word or a new word and the modifier in the syntax analysis result of the comment belongs to the specified lexicon, the viewpoint category corresponding to the comment is determined according to the fact that the syntax analysis result of the comment includes the seed word or the new word, so that data pruning can be performed on the comment for the specified object, and accuracy and efficiency of comment analysis can be improved. The implementation mode can carry out data pruning in different modes aiming at different syntactic relations so as to further improve the accuracy of comment analysis.
As one example of this implementation, the words in the specified thesaurus may all be adjectives.
As one example of this implementation, the words in the specified thesaurus may be positive or negative words. For example, the specified thesaurus may include 5000 recognition words and derogation words that are commonly used in chinese.
In step S15, the emotional tendency of the designated object in each viewpoint type is determined from the result of the syntactic analysis of the comment corresponding to each viewpoint type.
The method includes the steps of extracting new words from comments of a specified object, performing word segmentation processing on the comments according to the new words to obtain word segmentation results of the comments, performing syntactic analysis on the word segmentation results of the comments to obtain syntactic analysis results of the comments, determining viewpoint categories corresponding to the comments according to the condition that the syntactic analysis results of the comments contain seed words or the new words, and determining emotional tendency of the specified object in each viewpoint category according to the syntactic analysis results of the comments corresponding to each viewpoint category.
In a possible implementation manner, determining a viewpoint category corresponding to the comment according to a case that a result of the syntactic analysis of the comment includes a seed word or a new word includes: when the result of the syntactic analysis of the comment contains a seed word, the viewpoint category corresponding to the seed word is determined as the viewpoint category corresponding to the comment. For example, if the result of parsing a comment of a certain drama video includes a seed word "clothing", and the viewpoint category corresponding to the seed word "clothing" is "production", it may be determined that the viewpoint category corresponding to the comment is "production".
Fig. 2 shows an exemplary flowchart of comment analyzing method step S14 according to an embodiment of the present disclosure. As shown in fig. 2, step S14 may include step S141 and step S142.
In step S141, in a case where the result of the syntactic analysis of the comment contains a new word and does not contain a seed word, a seed word matching the new word is determined.
In step S142, the viewpoint category corresponding to the seed word matching the new word is determined as the viewpoint category corresponding to the comment.
For example, if the result of syntactic analysis of a comment does not include a seed word but includes the new word "dress", the seed word matching the new word "dress" may be determined as "dress", and the viewpoint category "production" corresponding to the seed word "dress" may be determined as the viewpoint category corresponding to the comment.
Fig. 3 shows an exemplary flowchart of determining a seed word matching the new word in step S141 of the comment analyzing method according to an embodiment of the present disclosure. As shown in fig. 3, step S141 may include steps S1411 to S1413.
In step S1411, a feature vector of the new word is determined according to the probability that each word in the specified word list and the new word appear in the same comment.
For example, a given vocabulary includes the words Y1 through Ym, where m is an integer greater than 1. m may be a larger integer, i.e., the number of words in the given vocabulary is larger. The new word C appears in 100 comments, and in the 100 comments, the number of occurrences of the word Y1 is N1, the number of occurrences of the word Y2 is N2, … …, and the number of occurrences of the word Ym is Nm, so that the feature vector of the new word C can be determined according to N1/100, N2/100, … …, Nm/100.
In step S1412, the feature vector of the seed word is determined according to the probability that each word in the designated word list and the seed word appear in the same comment.
For example, the specified vocabulary includes the words Y1 through Ym, with the seed word D appearing in 150 comments. In the 150 comments, the number of occurrences of the word Y1 is K1, the number of occurrences of the word Y2 is K2, … …, and the number of occurrences of the word Ym is Km, so that the feature vector of the seed word D can be determined according to K1/150, K2/150, … …, Km/150.
In step S1413, the seed word having the highest similarity to the feature vector of the new word is determined as the seed word matching the new word.
In this embodiment, according to the feature vector of the new word and the feature vectors of the seed words, the seed word with the highest similarity to the feature vector of the new word can be determined from the seed words.
Fig. 4 shows an exemplary flowchart of the comment analyzing method step S15 according to an embodiment of the present disclosure. As shown in fig. 4, step S15 may include steps S151 to S153.
In step S151, for any viewpoint category, the result of the syntax analysis of the comment corresponding to the viewpoint category is converted into a two-dimensional matrix.
In a possible implementation manner, for any viewpoint category, the syntactic analysis result of the comment corresponding to the viewpoint category can be converted into a two-dimensional matrix through a word2vec model by a Text-CNN algorithm.
In step S152, the two-dimensional matrix is input into the convolutional neural network, and the matching features of all convolutional kernels are extracted through the maximum pooling layer of the convolutional neural network.
In step S153, the emotional tendency of the designated object in the viewpoint category is determined based on the matching features.
In this embodiment, the convolutional neural network may perform a proportional analysis of emotional tendencies on the extracted syntactic analysis result, so as to obtain emotional tendencies of the designated object in the viewpoint category.
In one possible implementation, after obtaining the matching features of all convolution kernels, the emotional tendency of the specified object in the viewpoint category can be obtained through the full connection layer and softmax.
Fig. 5 is a schematic diagram illustrating a method for comment analysis according to an embodiment of the present disclosure, in which emotional tendencies of a designated object in various viewpoint categories are determined. As shown in fig. 5, the matching features may be determined from the maximum value after convolution with each convolution kernel. The emotional tendency of the designated object in each perspective category may be positive, negative, or neutral.
Fig. 6a to 6c are schematic diagrams illustrating emotional tendencies of movies or video-series videos in various viewpoint categories in a comment analysis method according to an embodiment of the present disclosure. Wherein, fig. 6a shows a schematic diagram of the emotional tendency of a movie or tv series video in the viewpoint category "overall rating" and "actor"; FIG. 6b shows a schematic diagram of the emotional tendency of a movie or a video of the TV series in the viewpoint categories "drama" and "production"; fig. 6c shows a schematic diagram of the emotional tendency of a movie or tv series video in the viewpoint categories "visual sound effect" and "scene".
Fig. 7a and 7b are schematic diagrams illustrating emotional tendencies of a variety video in various viewpoint categories in a comment analysis method according to an embodiment of the present disclosure. Wherein, fig. 7a shows a schematic diagram of the emotional tendency of the comprehensive art video in the viewpoint category "overall evaluation" and "character"; fig. 7b shows a schematic diagram of emotional tendency of the variety video in the viewpoint categories "link setting" and "production".
In one possible implementation, the method further includes: determining typical words of various viewpoint categories; and taking the typical word and the word with the similarity larger than a first threshold value as seed words.
As an example of this implementation, when the designated object is a video, a set of word2vec models suitable for video comments may be trained using comments of a video such as 3.5 billion visual comprehensions, and each word may be represented as a low-dimensional real vector of a fixed length by using the trained word2vec model, so that semantic information of the word may be considered, and the distances of words with similar semantics in a vector space may be closer.
As an example of this implementation, the representative words of the respective viewpoint categories may be determined from the representative words manually selected for each viewpoint category. For example, the number of typical words for each point of view category may be 1 or 2.
As an example of this implementation, cosine distances of the respective words in the first thesaurus and typical words of the respective viewpoint categories may be calculated, and the cosine distances may be taken as the similarity. For any viewpoint category, the typical word of the viewpoint category and the word with similarity greater than the first threshold with the typical word of the viewpoint category can be used as seed words. For example, a typical word of the viewpoint category H is the word H1, and if the similarity between the word H2 and the word H1 is greater than the first threshold value and the similarity between the word H3 and the word H1 is greater than the first threshold value, the word H1, the word H2, and the word H3 may be determined as seed words of the viewpoint category H.
Fig. 8 shows an exemplary flowchart of comment analyzing method step S11 according to an embodiment of the present disclosure. As shown in fig. 8, step S11 may include step S111 and step S112.
In step S111, adjacent character segmentation is performed on the comment for the specified object, and a segmentation result is obtained.
In a possible implementation manner, an offsettattribute class of Lucene TokenStream can be adopted to perform adjacent character segmentation on the comment aiming at the specified object, so as to obtain a segmentation result.
In step S112, a new word is extracted from the comment according to the degree of solidification and the degree of freedom of the cutting result.
In the present embodiment, the degrees of solidification and the degrees of freedom of the respective words in the cutting result may be calculated according to the method of calculating the degrees of solidification and the degrees of freedom in the related art.
In one possible implementation, the word a may be determined as a new word if the degree of solidification of the word a in the cutting result is greater than the second threshold and the degree of freedom is greater than the third threshold.
Fig. 9 shows a block diagram of a comment analyzing apparatus according to an embodiment of the present disclosure. As shown in fig. 9, the apparatus includes: an extraction module 91, configured to extract a new word from the comment for the specified object; the word segmentation processing module 92 is used for performing word segmentation processing on the comment according to the new word to obtain a word segmentation result of the comment; the syntactic analysis module 93 is configured to perform syntactic analysis on the word segmentation result of the comment to obtain a syntactic analysis result of the comment; a first determining module 94, configured to determine a viewpoint category corresponding to the comment according to a situation that a result of the syntactic analysis of the comment includes a seed word or a new word; and a second determining module 95, configured to determine, according to the result of the syntactic analysis of the comment corresponding to each viewpoint category, an emotional tendency of the designated object in each viewpoint category.
Fig. 10 illustrates an exemplary block diagram of a comment analyzing apparatus according to an embodiment of the present disclosure. As shown in fig. 10:
in one possible implementation, the first determining module 94 includes: a first determining sub-module 941, configured to determine, when a result of syntactic analysis of the comment includes a seed word, a viewpoint category corresponding to the seed word as a viewpoint category corresponding to the comment.
In one possible implementation, the first determining module 94 includes: a second determining sub-module 942, configured to determine, in a case that the result of the syntactic analysis of the comment includes a new word and does not include a seed word, a seed word that matches the new word; a third determining sub-module 943, configured to determine a viewpoint category corresponding to the seed word matching the new word as a viewpoint category corresponding to the comment.
In one possible implementation, the second determining sub-module 942 includes: the first determining unit is used for determining the feature vector of the new word according to the probability that each word in the appointed word list and the new word appear in the same comment; the second determining unit is used for determining the feature vector of the seed word according to the probability that each word in the appointed word list and the seed word appear in the same comment; and a third determining unit, configured to determine the seed word with the highest similarity to the feature vector of the new word as the seed word matched with the new word.
In one possible implementation, the second determining module 95 includes: a conversion sub-module 951 configured to convert a result of syntax analysis of a comment corresponding to an arbitrary viewpoint type into a two-dimensional matrix; the first extraction sub-module 952 is configured to input the two-dimensional matrix into a convolutional neural network, and extract matching features of all convolutional kernels through a maximum pooling layer of the convolutional neural network; and a fourth determining submodule 953 configured to determine an emotional tendency of the designated object in the viewpoint category according to the matching features.
In one possible implementation, the parsing module 93 is configured to: and carrying out syntactic analysis on the participle result of the comment according to the negative word, the name subject, the modifier and the direct object to obtain a syntactic analysis result of the comment.
In one possible implementation, the first determining module 94 is configured to: and determining the viewpoint type corresponding to the comment according to the condition that the syntactic analysis result of the comment contains the seed word or the new word when the syntactic analysis result of the comment contains the seed word or the new word and the modifier in the syntactic analysis result of the comment belongs to the specified word bank.
In one possible implementation, the apparatus further includes: a third determining module 96, configured to determine typical words of each viewpoint category; and a fourth determining module 97, configured to use the typical word and the word with similarity greater than the first threshold as the seed word.
In one possible implementation, the extraction module 91 includes: the cutting submodule 911 is configured to perform adjacent character cutting on the comment of the specified object to obtain a cutting result; and a second extraction sub-module 912, configured to extract new words from the comments according to the degree of solidification and the degree of freedom of the cutting result.
The method includes the steps of extracting new words from comments of a specified object, performing word segmentation processing on the comments according to the new words to obtain word segmentation results of the comments, performing syntactic analysis on the word segmentation results of the comments to obtain syntactic analysis results of the comments, determining viewpoint categories corresponding to the comments according to the condition that the syntactic analysis results of the comments contain seed words or the new words, and determining emotional tendency of the specified object in each viewpoint category according to the syntactic analysis results of the comments corresponding to each viewpoint category.
FIG. 11 is a block diagram illustrating an apparatus 800 for review analysis in accordance with an exemplary embodiment. For example, the apparatus 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 11, the apparatus 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The apparatus 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the device 800 to perform the above-described methods.
FIG. 12 is a block diagram illustrating an apparatus 1900 for review analysis according to an exemplary embodiment. For example, the apparatus 1900 may be provided as a server. Referring to fig. 12, the device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by the processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The device 1900 may also include a power component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input/output (I/O) interface 1958. The device 1900 may operate based on an operating system stored in memory 1932, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the apparatus 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A review analysis method, comprising:
extracting new words from the comments for the specified object;
performing word segmentation processing on the comment according to the new word to obtain a word segmentation result of the comment;
performing syntactic analysis on the word segmentation result of the comment to obtain a syntactic analysis result of the comment;
under the condition that the syntactic analysis result of the comment contains a new word and does not contain a seed word, determining a feature vector of the new word according to the probability that each word in a specified word list and the new word appear in the same comment, determining a feature vector of the seed word according to the probability that each word in the specified word list and the seed word appear in the same comment, determining the seed word with the highest similarity with the feature vector of the new word as a seed word matched with the new word, and determining a viewpoint category corresponding to the seed word matched with the new word as a viewpoint category corresponding to the comment, wherein the seed word refers to a typical word of each viewpoint category and a word with the similarity with the typical word larger than a first threshold value;
and determining the emotional tendency of the specified object in at least two viewpoint categories according to the syntactic analysis results of the comments corresponding to the at least two viewpoint categories.
2. The method of claim 1, wherein determining the emotional tendency of the designated object in each viewpoint category according to the result of the syntactic analysis of the comment corresponding to each viewpoint category comprises:
for any viewpoint category, converting a syntactic analysis result of the comment corresponding to the viewpoint category into a two-dimensional matrix;
inputting the two-dimensional matrix into a convolutional neural network, and extracting the matching characteristics of all convolutional kernels through the maximum pooling layer of the convolutional neural network;
and determining the emotional tendency of the specified object in the viewpoint category according to the matching features.
3. The method of claim 1, wherein parsing the word segmentation result of the comment to obtain a parsing result of the comment comprises:
and carrying out syntactic analysis on the participle result of the comment according to the negative word, the name subject, the modifier and the direct object to obtain a syntactic analysis result of the comment.
4. The method of claim 1, wherein extracting new words from comments directed to a specified object comprises:
carrying out adjacent character cutting on the comments aiming at the specified object to obtain a cutting result;
and extracting new words from the comments according to the degree of solidification and the degree of freedom of the cutting result.
5. A comment analyzing apparatus characterized by comprising:
the extraction module is used for extracting new words from the comments aiming at the specified object;
the word segmentation processing module is used for carrying out word segmentation processing on the comment according to the new word to obtain a word segmentation result of the comment;
the syntactic analysis module is used for carrying out syntactic analysis on the word segmentation result of the comment to obtain a syntactic analysis result of the comment;
a first determining module, configured to, when a syntactic analysis result of the comment includes a new word and does not include a seed word, determine a feature vector of the new word according to a probability that each word in a specified word list and the new word appear in the same comment, determine a feature vector of a seed word according to a probability that each word in the specified word list and the seed word appear in the same comment, determine a seed word having a highest similarity with the feature vector of the new word as the seed word matching the new word, and determine a viewpoint category corresponding to the seed word matching the new word as the viewpoint category corresponding to the comment, where the seed word refers to a typical word of each viewpoint category and a word having a similarity with the typical word greater than a first threshold;
and the second determining module is used for determining the emotional tendency of the specified object in at least two viewpoint categories according to the syntactic analysis result of the comments corresponding to the at least two viewpoint categories.
6. The apparatus of claim 5, wherein the second determining module comprises:
the conversion submodule is used for converting the syntactic analysis result of the comment corresponding to any viewpoint category into a two-dimensional matrix;
the first extraction submodule is used for inputting the two-dimensional matrix into a convolutional neural network and extracting the matching characteristics of all convolutional kernels through the maximum pooling layer of the convolutional neural network;
and the fourth determining submodule is used for determining the emotional tendency of the specified object in the viewpoint category according to the matching features.
7. The apparatus of claim 5, wherein the syntax analysis module is configured to:
and carrying out syntactic analysis on the participle result of the comment according to the negative word, the name subject, the modifier and the direct object to obtain a syntactic analysis result of the comment.
8. The apparatus of claim 5, wherein the extraction module comprises:
the cutting submodule is used for cutting adjacent characters of the comments aiming at the specified object to obtain a cutting result;
and the second extraction submodule is used for extracting new words from the comments according to the degree of solidification and the degree of freedom of the cutting result.
9. A comment analyzing apparatus characterized by comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the method of any one of claims 1 to 4.
10. A non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method of any of claims 1 to 4.
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| CN113327140B (en) * | 2021-08-02 | 2021-10-29 | 深圳小蝉文化传媒股份有限公司 | Video advertisement putting effect intelligent analysis management system based on big data analysis |
| CN114662469B (en) * | 2022-02-25 | 2023-04-07 | 北京百度网讯科技有限公司 | Emotion analysis method and device, electronic equipment and storage medium |
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