CN102122297A - Semantic-based Chinese network text emotion extracting method - Google Patents
Semantic-based Chinese network text emotion extracting method Download PDFInfo
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Abstract
The invention provides a method capable of accurately extracting network text emotion information. The method comprises the following steps of: (1) detecting special symbols such as emotion symbols, abbreviations, acronyms, interjections and the like in a text; (2) pre-processing the plain text by using a professional syntax analyzer to obtain a basic syntactic relationship of sentences; (3) analyzing words separated from the sentences, and endowing the words with emotion vectors; (4) obtaining phrase emotion vectors according to the types of different phrases and the corresponding emotion rules; and (5) obtaining final emotion vectors of the sentences according to different types of the sentences by combining the phrase emotion vectors. The network text emotion extracting method can comprehensively and detailedly identify the emotion types, and has high accuracy.
Description
(1) technical field
The present invention relates to a kind of network text emotion information extracting method, relate generally to natural language processing field and emotion and calculate the field.
(2) background technology
Natural language is one of human distinctive communication means, and along with Internet development, natural language also becomes the important means of internet exchange, and derives a kind of emerging language form gradually: netspeak.Network text information is containing abundant emotion information, corresponding the corresponding psychological condition of user, so the text emotion research of extracting is calculated and there is significance in the intelligent interaction field in emotion.It makes computing machine can know from experience human happiness, anger, grief and joy from text message and makes appropriate reaction, can be used for interactive system, bionic proxy interactive system.And how can accurately and effectively user's input language be carried out emotion recognition and classification, become the huge challenge that man-machine interaction and personalized computer realm face.
And the field that present Chinese natural language treatment technology relates to emotion information has only semantic tendency to calculate, and promptly passes judgement on ambiguous identification.This is for accurate analysis user behavior and to understand user feeling be far from being enough.In addition, netspeak has that following feature (1) does not have that complicated sentence formula (2) focuses on speed but not correct spelling (3) network flow langs (4) that use frequently use emoticon, abbreviation, breviary more, must set up the accurately emotion of identification user expression of corresponding text transaction module at these features.
The emotion computation model is the mutual key components of man-machine emotion, its basis and at all be understanding and expression to natural mood essence.Ekman is subjected to the influence of darwin's theory, proposes to comprise the computation model of six kinds of basic emotions: " indignant ", " detest ", " fear ", " happiness ", " sadness " and " surprised ".And the difference of these six kinds of emotions between cultural tradition is very little, has very strong versatility.
Aspect the information research of Chinese text emotion, most studies only is limited to and passes judgement on two kinds of feeling polarities at present, and lacks the corresponding model that netspeak is handled, and this has restricted the mutual development of Chinese man-machine emotion greatly.And can solve problems such as Chinese text emotion absence of information, discrimination are not high effectively in conjunction with the emotion extracting method of six kinds of basic emotion models of Ekman at the text-processing model of netspeak.Therefore, propose a kind of efficiently, careful Chinese text emotion information extracting method has very strong realistic meaning.
(3) summary of the invention
The technical problem to be solved in the present invention provides a kind of method that can accurately extract Chinese network text emotion information.
The invention provides a kind of network text emotion information extracting method, comprise following step based on semanteme and emotion calculating:
(1) emoticon in the detection text, abbreviation, breviary, special symbols such as interjection;
(2) utilize Chinese lexical analytic system ICTCLAS2011 that text is carried out pre-service, obtain the basic syntactic relation of sentence;
(3) speech of separating in the sentence is analyzed, from the emotion corpus, obtained corresponding sextuple emotion vector;
(4) according to the type of different phrases, formulate corresponding emotion rule, obtain the sextuple emotion vector of phrase;
(5) dissimilar according to sentence in conjunction with phrase emotion vector, obtain the final sextuple emotion vector of sentence.
In said method, the classification and the processing rule of the special symbol in the step (1) are as described below:
The I emoticon: in man-machine interaction, the use of emoticon more and more widely.The emotion information that emoticon comprised also is the most direct, the most accurately.Therefore, the individual processing to emoticon has great importance to detecting text emotion information.At first it is carried out the emotion classification for emoticon, adopt the method that manually marks to give corresponding emotion coefficient then, promptly it
<emotion?type，emotional?coefficient>：
The II abb., initialism, network flow lang: it is replaced to prototype, put into former sentence and wait for next step processing;
For example:
| CU | THX/3X | 886 | Idol | Dark reddish purple | Ash is normal |
| Goodbye | Thanks/ thanks | Good-by | I | So | Very |
III is for the punctuate of some repetitions, think that uppercase function is to strengthen emotion intensity, so also can give corresponding emotion coefficient＜emotional coefficient 〉, if contain these symbols in the sentence, need last emotion vector be multiply by corresponding emotion coefficient.
| Symbol | REALLY？ | MYGOD | ？？？？？？？ | ！！！！！ |
| The emotion coefficient | 1.2 | 1.3 | 1.4 | 1.4 |
Adopt following emotion rule for above three class special symbols
Emotion rule 1: if the emoticon of detecting is thought that then the emotion of emoticon representative promptly is the emotion of the text, and no longer text carried out next step analysis.The affective style of emoticon promptly is the affective style of sentence, and the strength factor of emoticon is the value of corresponding affective style in the emotion vector.
Emotion rule 2: if a plurality of emoticon, and belong to same classification, then select emotion strength factor maximum.
Emotion rule 3:, then select the leading emotion of affective style conduct of the emoticon of last appearance if emoticon belongs to different classifications.
In said method, the affective characteristics of speech is represented e=[anger in the described step (3) with sextuple emotion vector, detest fear, happiness, sadness, surprised], wherein each affective state all can adopt the method for manual mark to give a value between 0-1 for it.Such as e (defeating)=[0.2,0.1,0.1,0,0.6,0].And, think that following a few class speech contains emotion information according to Chinese grammar, semantic features: verb, adjective and other adverbial word of table degree level.
In said method, the corresponding phrase disposal route in the described step (4) is as follows:
According to the output result of syntactic analysis, can access the syntactic relation of sentence.And can obtain corresponding phrase according to these syntactic relations.According to the Chinese grammar custom phrase is divided into following a few class and handles respectively, adopt following emotion rule:
Emotion rule 4: for the adjective phrase is shape as the phrase of modifier+adjective structure, with the adjectival emotion vector of emotion coefficient adjustment of modifier.
Emotion rule 5: the phrase that constitutes for word such as adjective+noun, verb+noun, adverbial word+verb by two kinds of different parts of speech.At first according to both emotion vector α, β calculates its correlation coefficient r.Utilize the angle of two vectors to draw its related coefficient here:
If r＜0.5,, illustrate that the part of speech degree of correlation of two speech is little, at this time select the emotion vector of prevailing speech in the output phrase.If r＞=0.5 illustrates that the part of speech of two speech has certain correlativity, in this case,, select bigger value in two groups of emotion vectors respectively for every kind of affective style, obtain final emotion vector.
Emotion rule 6:, then the emotion value in the emotion vector of the word of its modification will be put 0 if there is negative word to occur.
In said method, the disposal route of described step (5) is as follows:
For the analysis of sentence, utilize analysis result for the phrase layer, finally draw the emotion vector of whole sentence in conjunction with the emotion vector of subject.Subject is handled according to following emotion rule the influence of sentence emotion vector:
Emotion rule 7: general personal pronoun (he, she, it etc.) and neuter (apple, the earth, sky), because itself do not contain emotion, so think that its emotion vector to whole sentence does not exert an influence.
Emotion rule 8:, then contrast the emotion vector of its emotion vector and phrase for the noun that contains emotion (bad egg, scoundrel, angel etc.).If for certain particular emotion type, both emotion values are not 0, then will get its maximal value as net result; If both emotion value has one to be 0 or all to be 0, then the emotion value is put 0.
And in computation process owing to there is a corresponding emotion coefficient, the emotion value that the emotion vector of final sentence has certain affective style is greater than 1 situation.And this situation meets normal emotional responses, and these expression people are expressing a kind of relatively intense emotion.
Problems such as the emotion recognition classification that Chinese network text emotion extracting method provided by the invention efficiently solves the existence of Chinese text emotion identification field is few, and recognition accuracy is not high.This method has following advantage: considered the characteristics that network text is intrinsic, set up the corresponding text transaction module, the corresponding emotion information of the more effective extraction of energy; Has very high universality in theory based on Chinese grammar and semantic emotion decision rule; Adopt the computation model of emotion vector, can be more comprehensively, the emotion of careful analysis text representation.
(4) description of drawings
Fig. 1 Chinese network text emotion is extracted block diagram
(5) embodiment
In order to set forth purpose of the present invention, technical scheme and advantage more clearly,, the network text emotion identification method of three embodiment is further described below in conjunction with accompanying drawing.Should be appreciated that specific embodiment described herein only in order to explanation the present invention, and be not used in qualification the present invention.
Basic thought of the present invention is by to network text being carried out grammer and pre-service semantically, obtains the basis relation of sentence; According to the emotion corpus of artificial mark, corresponding composition is carried out the assignment of emotion vector; Finally obtain the emotion vector of sentence again by the emotion rule of definition.
According to above thought, process flow diagram of the present invention as shown in Figure 1.
Recognition methods below by three specific embodiment explanation network text emotions.
(1) you can get and entertain me carefully specifically, otherwise I will kill your Kazakhstan O (O of ∩ _ ∩)
(2) today, my ash was normal sad!
(3) this scoundrel has suffered due punishment finally!
According to the Chinese lexical analytic system ICTCLAS2011 of doctor's Zhang Huaping development, can obtain the basic structure and the part-of-speech tagging of sentence.
(1) at first it is carried out emoticon and breviary, abbreviation inspection, can obtain emoticon O (O of ∩ _ ∩), can obtain corresponding emotion strength factor from the emotion corpus is 0.8, according to emotion rule 1, the emotion vector that can obtain this sentence is
e＝[0，0，0，0.8，0，0]
Therefore, the represented affective style of this sentence is glad, and corresponding strength is 0.8.And from the psychological feelings of actual persons, this sentence itself promptly is to express a kind of happiness, glad sensation, and is consistent with analysis result.The emotion rule that this explanation is formulated has feasibility.
(2) at first it is detected, " ash is normal " this cyberspeak is replaced by written language " very ".Utilize Words partition system that it is handled then, can obtain following result:
Today/t I/rr very/d /ude1 is sad/a/y
Therefrom extract Main Ingredients and Appearance: subject: I (pronoun), adverbial word+adjective phrase: very sad.
At first from the emotion corpus, extract corresponding emotion vector." very " belong to degree adverb, its emotion coefficient is 1.4; The emotion vector of " sad " is e=[0,0,0.2,0,0.8,0].So the emotion of this phrase vector is e=[0,0,0.28,0,1.12,0].
And subject is general personal pronoun for " I ", and according to emotion rule 7, it does not have influence to net result.
Can draw this sentence at last and express fear of a little and strong sadness, its emotion vector is
e＝[0，0，0.28，0，1.12，0]
(3) at first sentence is detected, detected the repetition punctuate, its emotion coefficient is 1.4; Then text is carried out pre-service, as follows:
This/rz scoundrel/n finally/d suffers/v/ule should have/v /ude1 punishment/vn
Therefrom can extract main analysis composition: subject: scoundrel's (containing the emotion noun); Verb+noun phrase: pay the penalty.
At first from the emotion corpus, extract corresponding emotion vector.The emotion vector that " suffers " is e1=[0,0,0.3,0,0.7,0]; The emotion vector of " punishment " is e2=[0.1,0.2,0.3, and 0.0.4.0].Calculate its correlation coefficient r=0.8873＞0.5, therefore obtain the vectorial e3=[0.1 of being of emotion of this phrase, 0.2,0.3,0,0.7,0] according to emotion rule 5.
And subject " scoundrel " is the noun that contains emotion, obtains emotion vector e4=[0.4 from the emotion corpus, 0.4,0.2,0,0,0]. therefore according to emotion rule 8, then can obtain emotion vector e5=[0.4,0.3,0.3,0,0,0]. the emotion coefficient that is multiplied by the repetition punctuate again can get final emotion vector and be e=[0.56,0.42,0.42,0,0,0].
Expressed in this presentation of results the words indignant, detest, frightened emotion.And from people's psychological feelings angle analysis, detest, fear and indignant feelings to the scoundrel when people say the words are prevailing.
Should note and understand, under the situation that does not break away from the desired the spirit and scope of the present invention of accompanying Claim, can make various modifications and improvement the present invention of foregoing detailed description.Therefore, the scope of claimed technical scheme is not subjected to given any specific exemplary teachings and restriction.
Claims (6)
1. network text emotion information extracting method that calculates based on semanteme and emotion may further comprise the steps:
(1) special symbols such as the emoticon in the detection text, abbreviation, breviary, interjection;
(2) utilize Chinese lexical analytic system ICTCLAS2011 that text is carried out pre-service, obtain the basic syntactic relation of sentence;
(3) speech of separating in the sentence is analyzed, from the emotion corpus, obtained corresponding sextuple emotion vector;
(4) according to the type of different phrases,, obtain the sextuple emotion vector of phrase according to corresponding emotion rule;
(5) dissimilar according to sentence in conjunction with phrase emotion vector, obtain the final sextuple emotion vector of sentence.
2. method according to claim 1 is characterized in that, described step (1) comprises the emotion coefficient assignment of emoticon commonly used and repetition punctuate, is used to characterize its influence degree to the emotion vector.
3. method according to claim 1 is characterized in that, described step (1) is as follows for the processing rule of emoticon:
Emotion rule 1: if the emoticon of detecting is thought that then the emotion of emoticon representative promptly is the emotion of the text, and no longer text carried out next step analysis; The affective style of emoticon promptly is the affective style of sentence, and the strength factor of emoticon is the value of corresponding affective style in the emotion vector;
Emotion rule 2: if a plurality of emoticon, and belong to same classification, then select emotion strength factor maximum;
Emotion rule 3:, then select the leading emotion of affective style conduct of the emoticon of last appearance if emoticon belongs to different classifications.
4. method according to claim 1 is characterized in that, described step (3) comprises carries out sextuple emotion vector assignment to word, is used to characterize expressed affective style of word and emotion intensity.
5. method according to claim 1 is characterized in that, described step (4) is as follows for the processing rule of phrase:
Emotion rule 4: for the adjective phrase is shape as the phrase of modifier+adjective structure, with the adjectival emotion vector of emotion coefficient adjustment of modifier;
Emotion rule 5: the phrase that constitutes for word such as adjective+noun, verb+noun, adverbial word+verb by two kinds of different parts of speech; At first according to the emotion vector α of two kinds of different part of speech words, β calculates its correlation coefficient r, utilizes the angle of two vectors to draw its related coefficient
If r＜0.5 illustrates that the part of speech degree of correlation of two speech is little, at this time select the emotion vector of prevailing speech in the output phrase; If r＞=0.5 illustrates that the part of speech of two speech has certain correlativity, in this case,, select bigger value in two groups of emotion vectors respectively for every kind of affective style, obtain final emotion vector like this;
Emotion rule 6:, then the emotion value in the emotion vector of the word of its modification will be put 0 if there is negative word to occur.
6. method according to claim 1 is characterized in that, described step (5) is as follows for the processing rule of whole sentence:
Emotion rule 7: general personal pronoun is as him, she, its waits and neuter such as apple, the earth, and skies etc. are not because itself contain emotion, so think that its emotion vector to whole sentence does not exert an influence;
Emotion rule 8:, then contrast the emotion vector of its emotion vector and phrase for the noun that contains emotion (bad egg, scoundrel, angel etc.); If for certain particular emotion type, both emotion values are not 0, then will get its maximal value as net result; If both emotion value has one to be 0 or all to be 0, then the emotion value is put 0.
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Application publication date: 20110713 |