CN115545026A - Network emotion analysis system based on fine-grained emotion dictionary - Google Patents
Network emotion analysis system based on fine-grained emotion dictionary Download PDFInfo
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Abstract
The invention discloses a network emotion analysis system based on a fine-grained emotion dictionary, which comprises a text input unit, an emotion analysis unit, an emotion weighting unit, an emotion division unit and a visualization unit, wherein the text input unit is used for inputting a text; the emotion analysis unit is used for carrying out emotion analysis on the words of the text training set in the text input unit, comparing the emotion analysis mode with the emotion words of the plurality of types in the emotion dictionary unit, and obtaining the overall emotion direction of the text training set; the emotion dividing unit is used for subdividing the emotion directions of the text training set after emotion weighting and obtaining the emotion directions to be expressed by the text training set; the invention relates to the technical field of emotion analysis of text words; according to the network emotion analysis system based on the fine-grained emotion dictionary, the emotion direction of words in a text is determined, then the emotion direction is subdivided according to the emotion, the emotion intensity value can be determined according to the position of a degree adverb, and emotion information contained in the text can be accurately analyzed.
Description
Technical Field
The invention relates to the technical field of emotion analysis of text words, in particular to a network emotion analysis system based on a fine-grained emotion dictionary.
Background
With the rapid development of social networks and electronic commerce, social networks and shopping platforms such as microblogs, weChat, QQ and Taobao bring great influence to the lives of people, more and more users like to make a self opinion on social media instead of just browsing and receiving information, in China, the microblogs have become a core platform for many young people to share and acquire information, the information contains personal emotions such as happiness, anger, grief and happiness, the emotion in the information can be analyzed to obtain the internal heart activity of the users and analyze the character characteristics of the users, and the analysis of the emotion of people on public events and social phenomena can better detect and control the progress of the events, so the emotion analysis of texts in the social media such as the microblogs is of great significance.
In the conventional network emotion analysis system, a fine-grained emotion dictionary is fused to assist in analyzing network emotions, a dictionary unit is constructed according to existing emotion words, and the words in the dictionary are compared with words split in a text to analyze the emotions, so that emotion analysis efficiency is improved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a network emotion analysis system based on a fine-grained emotion dictionary, and solves the problem that the emotion analysis of the existing network emotion analysis system is not accurate enough.
In order to achieve the purpose, the invention is realized by the following technical scheme: a network emotion analysis system based on a fine-grained emotion dictionary comprises a text input unit, an emotion analysis unit, an emotion weighting unit, an emotion division unit and a visualization unit;
the system comprises a text input unit, a network emotion analysis system and a text analysis unit, wherein the text input unit is used for inputting a text training set which needs emotion analysis into the network emotion analysis system and performing subsequent text training set processing;
the emotion analysis unit is used for carrying out emotion analysis on the words of the text training set in the text input unit, comparing the emotion analysis mode with the emotion words of the plurality of types in the emotion dictionary unit, and obtaining the overall emotion direction of the text training set;
the emotion weighting unit is used for carrying out emotion assignment on the words split in the text training set and obtaining the emotion direction pointed by the whole text training set;
the emotion dividing unit is used for subdividing the emotion directions of the text training set after emotion weighting and obtaining the emotion directions to be expressed by the text training set;
and the visualization unit is used for generating words in the text training set in a chart mode, distinguishing important words of emotion directions embodied in the text training set and then displaying the words in the chart mode.
Preferably, the system also comprises a central processing unit, an emotion dictionary unit, a text cleaning unit and a word frequency statistical unit;
the central processing unit is used for managing the attribute information of a plurality of service units in the network emotion analysis system and respectively managing configuration data files matched with the attribute information of the service units according to the attribute information of the service units;
the emotion dictionary unit is used for storing positive emotion words, negative emotion words and neutral emotion words, and classifying and storing a plurality of groups of words after distinguishing;
the text cleaning unit is used for splitting a text training set subjected to emotion analysis in the text input unit, removing invalid characters and splitting the text training set into a plurality of groups of words;
and the word frequency statistical unit is used for gathering the multiple groups of words split from the text training set in the text cleaning unit aiming at the same words and counting the number of the same words.
Preferably, in the emotion weighting unit, the positive emotion word score is set to be 1, the negative emotion word score is set to be-1, the neutral word is set to be 0, the positive word scores are all set to be 1, the negative word scores are all set to be-1, and the degree adverb is not changed, wherein two results can be generated at different positions of the negative word and the degree adverb, namely the negative word, the degree adverb and the emotion word, and the degree adverb, the negative word and the emotion word;
the calculation method for the negative word, the degree adverb and the emotional word is as follows:
P 1 ＝(x(K)+x(M)+x(O))×α，
the way to calculate "degree adverb + negation + emotion word" is as follows:
P 2 = (x (M) + x (K) + x (O)) × β, where P is 1 And P 2 Expressing the calculated emotional intensity value of the emotional words, x expressing the number of words, K expressing the weight of the negative words, O expressing the weight of the emotional words, M expressing the weight of the degree adverb before the emotional words, and alpha expressing P 1 Beta represents P 2 The total weight coefficient of (1).
Preferably, the emotion dividing unit subdivides the emotion of the words split in the text training set in the emotion direction, and the subdivision formula is as follows:
Preferably, the output end of the central processing unit is connected with the input ends of the emotion dictionary unit, the text input unit, the text cleaning unit, the word frequency statistical unit, the emotion analysis unit, the emotion weighting unit, the emotion dividing unit and the visualization unit;
the output end of the text input unit is connected with the input end of the text cleaning unit, the output end of the text cleaning unit is connected with the input end of the word frequency statistic unit, the output end of the word frequency statistic unit is connected with the input end of the emotion analysis unit, the output end of the emotion analysis unit is connected with the input end of the emotion weighting unit, the output end of the emotion weighting unit is connected with the input end of the emotion dividing unit, and the output end of the emotion dividing unit is connected with the input end of the visualization unit;
the emotion dictionary unit and the emotion analysis unit are connected in a two-way mode, and the emotion dictionary unit and the emotion division unit are connected in a two-way mode.
Preferably, the text cleaning unit comprises a first input module, a text word segmentation module, a character detection module and a first output module, wherein the output end of the first input module is connected with the input end of the text word segmentation module, the output end of the text word segmentation module is connected with the input end of the character detection module, and the output end of the character detection module is connected with the input end of the first output module.
Preferably, the emotion analysis unit comprises a second input module, a second word screening module, a second word classifying module, a second degree adverb judgment module and a second output module, wherein the output end of the second input module is connected with the input end of the second word screening module, the output end of the second word screening module is connected with the input end of the second word classifying module, the output end of the second word classifying module is connected with the input end of the second degree adverb judgment module, and the output end of the second degree adverb judgment module is connected with the input end of the second output module.
Preferably, the emotion weighting unit comprises a third input module, a word assignment module, a positive emotion word module, a neutral word module, a negative emotion word module and a third output module, wherein the output end of the third input module is connected with the input end of the word assignment module, the output ends of the word assignment module are connected with the input ends of the positive emotion word module, the neutral word module and the negative emotion word module, and the output ends of the positive emotion word module, the neutral word module and the negative emotion word module are connected with the input end of the third output module.
Preferably, the emotion classification unit comprises an input module IV, an emotion classification module, a happy emotion word module, a sad emotion word module, a fear emotion word module, a anger emotion word module and an output module IV, wherein the output end of the input module IV is connected with the input end of the emotion classification module, the output end of the emotion classification module is connected with the input ends of the happy emotion word module, the sad emotion word module, the fear emotion word module and the anger emotion word module, and the output ends of the happy emotion word module, the sad emotion word module, the fear emotion word module and the anger emotion word module are connected with the input end of the output module IV.
Preferably, the visualization unit comprises an input module five, a chart generation module, a chart editing module, a chart display module and an output module five, wherein the output end of the input module five is connected with the input end of the chart generation module, the output end of the chart generation module is connected with the input end of the chart editing module, the output end of the chart editing module is connected with the input end of the chart display module, and the output end of the chart display module is connected with the input end of the output module five.
The invention provides a network emotion analysis system based on a fine-grained emotion dictionary, which has the following beneficial effects compared with the prior art:
1. according to the network emotion analysis system based on the fine-grained emotion dictionary, scores can be carried out on positive emotion words, negative emotion words and neutral words through an emotion weighting unit, then the position of a degree adverb is determined in a text, different coefficients are set through the position of the degree adverb, the emotion tendency of the text is determined, the semantic emotion direction in the text can be determined according to the emotion tendency, and then the analysis result of subsequent emotion can be determined.
2. According to the network emotion analysis system based on the fine-grained emotion dictionary, various emotion directions in a text can be determined through the emotion dividing unit, and the emotion analysis result of the text can be determined more accurately on the basis of the emotion directions according to the intensity values of different emotions.
Drawings
FIG. 1 is a block diagram of a fine-grained emotion dictionary-based network emotion analysis system of the present invention;
FIG. 2 is a block diagram of a text washing unit system of the present invention;
FIG. 3 is a block diagram of an emotion analysis unit system according to the present invention;
FIG. 4 is a block diagram of an emotion weighting unit system of the present invention;
FIG. 5 is a system block diagram of an emotion classification unit of the present invention;
FIG. 6 is a visualization unit system block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.
Example (b):
referring to fig. 1-6, a network emotion analysis system based on a fine-grained emotion dictionary comprises a text input unit, an emotion analysis unit, an emotion weighting unit, an emotion division unit and a visualization unit;
the system comprises a text input unit, a network emotion analysis system and a text analysis unit, wherein the text input unit is used for inputting a text training set which needs emotion analysis into the network emotion analysis system and performing subsequent text training set processing;
the emotion analysis unit is used for carrying out emotion analysis on the words of the text training set in the text input unit, comparing the emotion analysis mode with the emotion words of the plurality of types in the emotion dictionary unit, and obtaining the overall emotion direction of the text training set;
the emotion weighting unit is used for carrying out emotion assignment on the words split in the text training set and obtaining the emotion direction pointed by the whole text training set;
the emotion dividing unit is used for subdividing the emotion directions of the text training set after emotion weighting and obtaining the emotion directions to be expressed by the text training set;
and the visualization unit is used for generating words in the text training set in a chart mode, distinguishing important words of emotion directions embodied in the text training set and then displaying the words in the chart mode.
In the embodiment, the system also comprises a central processing unit, an emotion dictionary unit, a text cleaning unit and a word frequency statistical unit;
the central processing unit is used for managing the attribute information of a plurality of service units in the network emotion analysis system and respectively managing configuration data files matched with the attribute information of the service units according to the attribute information of the service units;
the emotion dictionary unit is used for storing positive emotion words, negative emotion words and neutral emotion words, and classifying and storing a plurality of groups of words after distinguishing;
the text cleaning unit is used for splitting a text training set subjected to emotion analysis in the text input unit, removing invalid characters and splitting the text training set into a plurality of groups of words;
and the word frequency counting unit is used for gathering the plurality of groups of words split from the text training set in the text cleaning unit aiming at the same words and counting the number of the same words.
In this embodiment, in the emotion weighting unit, the positive emotion word score is set to 1, the negative emotion word score is set to-1, the neutral word is set to 0, the positive word scores are all set to 1, the negative word scores are all set to-1, and the degree adverb is not changed, where different positions of the negative word and the degree adverb can generate two results, namely "negative word + degree adverb + emotion word", or "degree adverb + negative word + emotion word";
the calculation method for the negative word, the degree adverb and the emotional word is as follows:
P 1 ＝(x(K)+x(M)+x(O))×α，
the way to calculate "degree adverb + negation + emotion word" is as follows:
P 2 = (x (M) + x (K) + x (O)) × β, where P is 1 And P 2 Expressing the calculated emotional intensity value of the emotional words, x expressing the number of words, K expressing the weight of the negative words, O expressing the weight of the emotional words, M expressing the weight of the degree adverb before the emotional words, and alpha expressing P 1 Beta represents P 2 The total weight coefficient is used for determining the emotional direction of the whole text training set, and the emotional tendency of the text training set is strengthened or weakened through different coefficients according to the position of the degree adverb in the text training set, so that more accurate emotional intensity is obtained.
In this embodiment, the emotion dividing unit performs emotion direction subdivision on the emotion of the word split in the text training set, and the subdivision formula is as follows:
In the embodiment, the output end of the central processing unit is connected with the input ends of the emotion dictionary unit, the text input unit, the text cleaning unit, the word frequency statistical unit, the emotion analysis unit, the emotion weighting unit, the emotion dividing unit and the visualization unit;
the output end of the text input unit is connected with the input end of the text cleaning unit, the output end of the text cleaning unit is connected with the input end of the word frequency statistical unit, the output end of the word frequency statistical unit is connected with the input end of the emotion analysis unit, the output end of the emotion analysis unit is connected with the input end of the emotion weighting unit, the output end of the emotion weighting unit is connected with the input end of the emotion dividing unit, and the output end of the emotion dividing unit is connected with the input end of the visualization unit;
and the emotion dictionary unit and the emotion analysis unit are connected in two ways, and the emotion dictionary unit and the emotion division unit are connected in two ways.
In this embodiment, the text cleaning unit includes a first input module, a text segmentation module, a character detection module, and a first output module, an output end of the first input module is connected to an input end of the text segmentation module, an output end of the text segmentation module is connected to an input end of the character detection module, an output end of the character detection module is connected to an input end of the first output module, the text cleaning unit performs word decomposition on the text training set through the text segmentation module so as to correspond to characters in an emotion dictionary, thereby determining an emotion direction, and redundant characters in the text training set can be deleted through the character detection module, thereby reducing the calculation amount of the system.
In this embodiment, the emotion analysis unit includes a second input module, a second word screening module, a second word classifying module, a second degree adverb judging module and a second output module, an output end of the second input module is connected to an input end of the second word screening module, an output end of the second word screening module is connected to an input end of the second word classifying module, an output end of the second word classifying module is connected to an input end of the second degree adverb judging module, an output end of the second degree adverb judging module is connected to an input end of the second output module, the words after decomposition are divided in different directions by the second word screening module, and the divided words are gathered by the second word classifying module, so that the emotion direction of the text training set is conveniently determined.
In this embodiment, the emotion weighting unit includes a third input module, a word assignment module, a positive emotion word module, a neutral word module, a negative emotion word module, and a third output module, an output end of the third input module is connected to an input end of the word assignment module, output ends of the word assignment module are connected to input ends of the positive emotion word module, the neutral word module, and the negative emotion word module, output ends of the positive emotion word module, the neutral word module, and the negative emotion word module are connected to an input end of the third output module, scores can be performed on the positive emotion words, the negative emotion words, and the neutral words through the word assignment module, and semantic emotion directions in the text are determined according to score results.
In this embodiment, the emotion classification unit includes a fourth input module, an emotion classification module, a happy emotion word module, a sadian emotion word module, a fear emotion word module, an angry emotion word module, and a fourth output module, an output end of the fourth input module is connected to an input end of the emotion classification module, output ends of the emotion classification module are connected to input ends of the happy emotion word module, the sadian emotion word module, the fear emotion word module, and the angry emotion word module, and output ends of the happy emotion word module, the sadian emotion word module, the fear emotion word module, and the angry emotion word module are connected to an input end of the fourth output module.
In this embodiment, the visualization unit includes a fifth input module, a fifth chart generation module, a fifth chart editing module, a fifth chart display module, and a fifth output module, an output end of the fifth input module is connected to an input end of the fifth chart generation module, an output end of the fifth chart generation module is connected to an input end of the chart editing module, an output end of the chart editing module is connected to an input end of the fifth chart display module, and an output end of the chart display module is connected to an input end of the fifth output module, so that the emotion direction of the text training set and the emotion to be expressed by the text training set can be intuitively sensed by the chart display module.
It should be noted that, in this document, relational terms such as first and second, and the like are only used for distinguishing one entity or operation from another entity or operation, and do not necessarily require or imply any actual relation or order between the entities or operations, and furthermore, the terms "comprise", or any other variation thereof are intended to cover a non-exclusive inclusion, so that a process, a method, an article, or an apparatus including a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A network emotion analysis system based on a fine-grained emotion dictionary is characterized by comprising a text input unit, an emotion analysis unit, an emotion weighting unit, an emotion division unit and a visualization unit;
the system comprises a text input unit, a network emotion analysis system and a text analysis unit, wherein the text input unit is used for inputting a text training set which needs emotion analysis into the network emotion analysis system and performing subsequent text training set processing;
the emotion analysis unit is used for carrying out emotion analysis on the words of the text training set in the text input unit, comparing the emotion analysis mode with the emotion words of the plurality of types in the emotion dictionary unit, and obtaining the overall emotion direction of the text training set;
the emotion weighting unit is used for carrying out emotion assignment on the words split in the text training set and obtaining the emotion direction pointed by the whole text training set;
the emotion dividing unit is used for subdividing the emotion directions of the text training set after emotion weighting and obtaining the emotion directions to be expressed by the text training set;
and the visualization unit is used for generating the words in the text training set in a chart mode, distinguishing the important words of the emotion directions in the text training set and displaying the words in the chart mode.
2. The network emotion analysis system based on the fine-grained emotion dictionary, as recited in claim 1, further comprising a central processing unit, an emotion dictionary unit, a text cleansing unit and a word frequency statistic unit;
the central processing unit is used for managing the attribute information of a plurality of service units in the network emotion analysis system and respectively managing configuration data files matched with the attribute information of the service units according to the attribute information of the service units;
the emotion dictionary unit is used for storing positive emotion words, negative emotion words and neutral emotion words, and classifying and storing a plurality of groups of words after distinguishing;
the text cleaning unit is used for splitting a text training set subjected to emotion analysis in the text input unit, removing invalid characters and splitting the text training set into a plurality of groups of words;
and the word frequency counting unit is used for gathering the plurality of groups of words split from the text training set in the text cleaning unit aiming at the same words and counting the number of the same words.
3. The network emotion analysis system based on the fine-grained emotion dictionary, according to claim 1, characterized in that: in the emotion weighting unit, the positive emotion word score is set to be 1, the negative emotion word score is set to be-1, neutral words are set to be 0, positive word scores are all set to be 1, negative word scores are all set to be-1, degree adverbs are not changed, wherein two results can be generated at different positions of the negative word and the degree adverbs, namely, the negative word, the degree adverbs and the emotion word, and the degree adverbs, the negative word and the emotion word are also the same;
the calculation method for the negative word, the degree adverb and the emotional word is as follows:
P 1 ＝(x(K)+x(M)+x(O))×α，
the way to calculate "degree adverb + negation + emotion word" is as follows:
P 2 = (x (M) + x (K) + x (O)) × β, where P is 1 And P 2 Expressing the calculated emotional intensity value of the emotional words, x expressing the number of words, K expressing the weight of the negative words, O expressing the weight of the emotional words, M expressing the weight of the degree adverb before the emotional words, and alpha expressing P 1 Beta represents P 2 The total weight coefficient of (1).
4. The network emotion analysis system based on the fine-grained emotion dictionary as claimed in claim 1, wherein the emotion dividing unit performs emotion direction subdivision on the emotion of words split in the text training set, and the subdivision formula is as follows:
5. The network emotion analysis system based on the fine-grained emotion dictionary as claimed in claim 1, wherein the output end of the central processing unit is connected with the input ends of the emotion dictionary unit, the text input unit, the text cleaning unit, the word frequency statistic unit, the emotion analysis unit, the emotion weighting unit, the emotion dividing unit and the visualization unit;
the output end of the text input unit is connected with the input end of the text cleaning unit, the output end of the text cleaning unit is connected with the input end of the word frequency statistic unit, the output end of the word frequency statistic unit is connected with the input end of the emotion analysis unit, the output end of the emotion analysis unit is connected with the input end of the emotion weighting unit, the output end of the emotion weighting unit is connected with the input end of the emotion dividing unit, and the output end of the emotion dividing unit is connected with the input end of the visualization unit;
the emotion dictionary unit and the emotion analysis unit are connected in a two-way mode, and the emotion dictionary unit and the emotion division unit are connected in a two-way mode.
6. The fine-grained emotional dictionary-based network emotion analysis system according to claim 1, wherein the text cleaning unit comprises a first input module, a text word segmentation module, a character detection module and a first output module, an output end of the first input module is connected with an input end of the text word segmentation module, an output end of the text word segmentation module is connected with an input end of the character detection module, and an output end of the character detection module is connected with an input end of the first output module.
7. The network emotion analysis system based on the fine-grained emotion dictionary as claimed in claim 1, wherein the emotion analysis unit comprises a second input module, a second word screening module, a second word classification module, a second degree adverb judgment module and a second output module, wherein an output end of the second input module is connected with an input end of the second word screening module, an output end of the second word screening module is connected with an input end of the second word classification module, an output end of the second word classification module is connected with an input end of the second degree adverb judgment module, and an output end of the second degree adverb judgment module is connected with an input end of the second output module.
8. The fine-grained emotion dictionary-based network emotion analysis system according to claim 1, wherein the emotion weighting unit comprises a third input module, a word assignment module, a positive emotion word module, a neutral word module, a negative emotion word module and a third output module, an output end of the third input module is connected with an input end of the word assignment module, an output end of the word assignment module is connected with input ends of the positive emotion word module, the neutral word module and the negative emotion word module, and output ends of the positive emotion word module, the neutral word module and the negative emotion word module are connected with an input end of the third output module.
9. The network emotion analysis system based on the fine-grained emotion dictionary is characterized in that the emotion dividing unit comprises an input module IV, an emotion classification module, a happy emotion word module, a sad emotion word module, a fear emotion word module, an anger emotion word module and an output module IV, the output end of the input module IV is connected with the input end of the emotion classification module, the output end of the emotion classification module is connected with the input ends of the happy emotion word module, the sad emotion word module, the fear emotion word module and the anger emotion word module, and the output ends of the happy emotion word module, the sad emotion word module, the fear emotion word module and the anger emotion word module are connected with the input end of the output module IV.
10. The fine-grained emotion dictionary-based network emotion analysis system according to claim 1, wherein the visualization unit comprises a fifth input module, a fifth chart generation module, a fifth chart editing module, a fifth chart display module and a fifth output module, an output end of the fifth input module is connected with an input end of the fifth chart generation module, an output end of the fifth chart generation module is connected with an input end of the fifth chart editing module, an output end of the fifth chart editing module is connected with an input end of the fifth chart display module, and an output end of the fifth chart display module is connected with an input end of the fifth output module.
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