CN106598944B - A kind of civil aviaton's security public sentiment sentiment analysis method - Google Patents

A kind of civil aviaton's security public sentiment sentiment analysis method Download PDF

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CN106598944B
CN106598944B CN201611062208.1A CN201611062208A CN106598944B CN 106598944 B CN106598944 B CN 106598944B CN 201611062208 A CN201611062208 A CN 201611062208A CN 106598944 B CN106598944 B CN 106598944B
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emotion
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韩萍
李杉
贾云飞
牛勇钢
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Civil Aviation University of China
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Abstract

A kind of civil aviaton's public sentiment sentiment analysis method.It includes being retrieved, pre-processed and being segmented operation to the microblogging text for including civil aviaton's security public sentiment keyword on internet；Construct dictionary；It gives a mark to microblogging, obtains the microblog emotional score value；Subjective and objective differentiation is carried out to microblogging according to emotion score value, obtains the microblogging to the Threat score value of safety of civil aviation；Determine the speech in microblogging text to the Threat grade of safety of civil aviation according to Threat score value.The present invention by text semantic and microblogging emoticon combine in the way of determine the emotion score of microblogging text, overcome the limitation of dictionary and semantic rules, improve emotion score accuracy of judgement degree.The characteristics of making full use of microblogging text determines Threat more reasonable grade.The present invention is different from machine learning method, does not need to be trained with extensive tagged data, therefore be more suitable for real time data stream process.

Description

A kind of civil aviaton's security public sentiment sentiment analysis method
Technical field
The invention belongs to the text emotion analysis technical fields in natural language processing, more particularly to a kind of civil aviaton's security Public sentiment sentiment analysis method.
Background technique
In the Internet era of information rapid expansion, more and more users tend to share oneself by internet Viewpoint or experience, so there is a large amount of short texts with subjective emotional color in social networks.Sina weibo be big The information that crowd provides amusement and leisure service for life shares and intercommunion platform, and the active users of Sina weibo are maintained at 200,000,000 at present The advantages of controlling, inheriting the forms such as traditional forum, blog enables information to real-time, quickly in conjunction with mobile terminals such as mobile phones Publication and acquisition.Microblogging integrates amusement, social activity, marketing, from the social demand for meeting people's " weak relationship " gradually Developing becomes popular public opinion platform, becomes a most important real time information source and a kind of network that influence power is increasingly enhanced Public Opinion Transmission center, more and more mechanisms and public figure are issued or are propagated information by microblogging.
Sentiment analysis is the process that the text with emotional color is handled, analyzed and applied, and is at natural language Compared with the research field in forward position in reason.It is a kind of concrete application in conjunction with existing many research achievements, and as new network social intercourse The microblogging of media combines, and has important practical value.The main purpose of microblog emotional analysis is exactly to know from micro-blog information Other subjective information excavates viewpoint and attitude that user holds the comment informations such as product, news, focus incident.
Some negative effects are brought while civil aviaton field, network public opinion height liberalization, such as issue false prestige Coerce speech, rumour, extreme language etc..By carrying out emotional orientation analysis to microblogging text relevant to civil aviaton, can filter There is the microblogging of threat to safety of civil aviation out, so that locking has the emphasis user of criminal tendency, is pushed to related public security department in time It is handled.In addition to this, there are also the applications of the following aspects for text emotion analysis: prediction box office receipts, shares changing tendency, city Field dynamic etc..Therefore microblog text affective proneness analysis is had a very important significance.
Currently, Chinese text sentiment analysis method mainly has based on semantic understanding and based on two class method of machine learning.But Both methods is applied to be primarily present following problems in microblog emotional analysis: the 1. building benchmark of the method based on semantic understanding The method passed judgement on dictionary and define display rule carries out pattern match to corpus, complicated, the irregular microblogging for expression way There is significant limitation in text-processing.2. the method based on machine learning is limited to selection and the corpus scale of feature, And it is easy to produce over-fitting effect, it is not suitable for real-time high-volume text-processing.
Summary of the invention
To solve the above-mentioned problems, the purpose of the present invention is to provide a kind of civil aviaton's security public sentiment sentiment analysis methods.
In order to achieve the above object, civil aviaton's security public sentiment sentiment analysis method provided by the invention includes carrying out in order The following steps:
(1) behaviour is retrieved, pre-processed and segmented to the microblogging text on internet including civil aviaton's security public sentiment keyword Make；
(2) building is for all kinds of dictionaries needed for the analysis of microblogging text semantic, construction method be divided into choose existing dictionary and The mode independently constructed；
(3) dictionary constructed according to above-mentioned steps (2) is given a mark to the above-mentioned microblogging after step (1) participle, is obtained The emotion score value of the microblogging；
(4) the emotion score value according to obtained in step (3) carries out subjective and objective differentiation to microblogging, exists for filtering news report Interior objective microblogging retains the microblogging for having subjectivity, finally obtains the microblogging to the Threat score value of safety of civil aviation；
(5) determine the speech in microblogging text to the Threat of safety of civil aviation according to the Threat score value that step (4) obtains Then grade filters out the high emphasis personnel of Threat grade, and reports and submits relevant departments as warning information.
In step (1), it is described on internet include civil aviaton's security public sentiment keyword microblogging text retrieved, The method of pretreatment and participle operation is: including the microblogging text of civil aviaton's security public sentiment keyword on crawl internet, from these Retrieval is related to the keyword of civil aviaton's security public sentiment in microblogging text, and keyword is divided into two class of place word and behavior word, retrieval Strategy is divided into single word retrieval and combined retrieval two ways；Then pretreatment operation is carried out to above-mentioned search result, to go The noise information including user's pet name, topic label, spcial character when except web page interlinkage, forwarding, reply microblogging, and extract table Feelings symbol；It is segmented later to above-mentioned by pretreated result using participle tool, participle tool is increased income using Java Participle tool Ansj.
In step (2), the dictionary includes sentiment dictionary, negative dictionary, modification dictionary, conjunction dictionary, emoticon Number dictionary, network hot word dictionary and civil aviaton's security public sentiment dictionary.
In step (3), the dictionary constructed according to above-mentioned steps (2), to above-mentioned micro- after step (1) participle Rich to give a mark, the method for obtaining the emotion score value of the microblogging includes the following steps:
1) it extracts from the above-mentioned microblogging text after step (1) participle or determines emotion word:
The method for extracting emotion word is the word and above-mentioned sentiment dictionary that will be obtained after participle in above-mentioned microblogging text It is matched with network hot word dictionary, if a certain word is present in above-mentioned two dictionary, is chosen for emotion word；
The method for determining emotion word is to the word not appeared in sentiment dictionary and network hot word dictionary using semantic Similarity based method carries out；Specific method is for two word w1And w2If word w1There are the n senses of a dictionary entry or concept: x1,x2…, xn, word w2There are the m senses of a dictionary entry or concept: y1,y2…,ym, it is specified that word w1And w2Similarity be that each senses of a dictionary entry or concept are similar The maximum value of degree, it may be assumed that
The former calculating formula of similarity of two justice are as follows:
Wherein, λ is positive variable element；d(x1,y2) indicate adopted original x1With adopted original y2Distance in hierarchical tree；
Each seed words in word w and positive emotion dictionary are subjected to similarity calculation by formula (1) and formula (2) and obtain the word With the similarity of front seed words, then seed words each in word w and negative emotion dictionary is subjected to similarity calculation and are somebody's turn to do The similarity of word and negative seed words finally obtains the Sentiment orientation value of word w by comparing the equal difference between them, meter It is as follows to calculate formula:
Wherein, piIndicate a certain positive emotion seed words, njIndicate a certain negative emotion seed words；Sentiment orientation value Sw's Value range is (- 1,1)；Given threshold T, by calculated Sentiment orientation value SwIt is compared with threshold value T, to determine word w Whether emotion word is belonged to；When | Sw| when > T, determine that word w is emotion word, the intensity of the emotion word is set to 10Sw；
2) the text emotion score of each microblogging clause in microblogging comprising above-mentioned emotion word is determined；
If 2.1) in microblogging clause include emotion word, and occur the negative word belonged in negative dictionary or modification before it When qualifier in dictionary, the text emotion score Sa of microblogging clause is calculated by following several situations:
E) degree adverb+emotion word, emotion word intensity change with adverbial word intensity, text emotion score are as follows:
Sa=Ma·ps·pa (4)
F) polarity of negative word+emotion word, emotion word changes, text emotion score according to the number of negative word are as follows:
Sa=(- 1)n·ps·pa (5)
G) degree adverb+negative word+emotion word, the reversion of emotion word polarity, and intensity changes with adverbial word intensity, text feelings Feel score are as follows:
Sa=(- 1) Ma·ps·pa (6)
H) negative word+degree adverb+emotion word, before appearing in degree adverb due to negative, after the reversion of emotion word polarity, Emotion word intensity more directly negates to be weakened, and introduces the first weight factor z1=0.5, text emotion score are as follows:
Sa=(- 1) Ma·ps·pa·z1 (7)
Wherein, ps indicates the intensity of emotion word, and pa indicates emotion word polarity, MaIndicate the intensity of degree adverb:
2.2) if comprising the adversative conjunction in conjunction dictionary in microblogging clause, microblogging clause belongs to compound sentence, it is contemplated that Feeling polarities transfer between sentence, the text emotion score of microblogging clause is calculated by following several situations:
D) turning relation: when occur in microblogging clause " still ", " however " etc. semantic reversion vocabulary when, previous microblogging clause Polarity will change, the integral polarity of the two microbloggings clause will be identical as the latter microblogging clause, introduce second power Repeated factor z2=-1, text emotion score are as follows:
Sen=z2Sen1+Sen2 (8)
E) progressive relationship: former and later two microbloggings clause's polarity is identical, enhanced strength, introduces third weight factor z3=1.5, Text emotion score are as follows:
Sen=z3(Sen1+Sen2) (9)
F) concession relationship: the polarity of the latter microblogging clause can invert, the polarity of whole sentence and previous microblogging clause phase Together, the 4th weight factor z is introduced4=-1, text emotion score are as follows:
Sen=Sen1+z4Sen2 (10)
Wherein, Sen1Indicate the text emotion score of previous microblogging clause, Sen2Indicate the text of the latter microblogging clause Emotion score；
3) emoticon score in microblogging is determined；
According to emoticon dictionary, the polarity and intensity of all emoticons in the microblogging are found, and records each expression The number of symbol；Enable NiFor the number of i-th of emoticon, eiFor the intensity of the emoticon, piFor the pole of the emoticon Property, then the emoticon score calculation formula in microblogging are as follows:
4) above-mentioned microblog text affective score and emoticon score are weighted summation, obtain each microblogging Emotion score value, formula are as follows:
S1=α scoreemo+β·scoretext (12)
Wherein, α, β are adjustable weight, and value range is (0,1), and alpha+beta=1 can be selected by the verifying of cross-beta collection α, β value when correct class probability maximum；scoretextIt is each microblogging clause text emotion for the text emotion score of the microblogging The average value of score.
In step (4), the emotion score value according to obtained in step (3) carries out subjective and objective differentiation to microblogging, uses Objective microblogging including filtering news report retains the microblogging for having subjectivity, finally obtains the microblogging to safety of civil aviation The method of Threat score value is:
Subjective and objective differentiation is carried out to microblogging text using following methods first:
1) for emotion score value S1=0 microblogging, if wherein including first person noun or pronoun, then it is assumed that be subjective micro- Otherwise blog article sheet is objective microblogging text；
2) for emotion score value S1≠ 0 microblogging, if the wherein special predicate word comprising news report or microblogging text In hop count at least 2 times, then it is assumed that be objective microblogging text, be otherwise subjective microblogging text；
The Threat score value of objective microblogging text is set as 0, and is calculated without Threat score value, is only calculated subjective The Threat score value of microblogging, shown in calculation formula such as formula (13):
Wherein, D indicates Threat score value, and range is between [- 10,10]；S1Indicate the emotion score value of microblogging text；S2< w1,w2> is that civil aviaton's security public sentiment threatens score, w1Indicate place word, w2Expression behavior word；
Civil aviaton's security public sentiment threatens score S2< w1,w2The calculating process of > is as follows: searching the behavior word in microblogging text w2, then judge the type of behavior word；When behavior word is Direct-type, civil aviaton's security public sentiment threatens score S2< w1, w2The value of > takes the intensity of behavior word；When behavior word is indirect-type, judge in the microblogging text whether and meanwhile deposit In place word, if existed simultaneously, civil aviaton's security public sentiment threatens score S2< w1,w2The value of > takes the strong of behavior word Degree threatens score S if do not existed simultaneously2< w1,w2> is 0.
In step (5), the Threat score value obtained according to step (4) determines the speech in microblogging text to the people The method of the Threat grade for safety of navigating is:
As Threat score value D > 0, which is positive emotion, belongs to safe speech, therefore without Threat grade determines；As Threat score value D≤0, determine that the microblogging text contains civil aviaton's security public sentiment keyword, and express Be Negative Affect, need to pay close attention to, then according to following Threat classification standard to microblogging text degree of impending etc. Grade determines；Threat classification standard is obtained from testing existing microblogging text, specific as follows:
It 1) is low Threat when -4.5≤D≤0；
It 2) is medium Threat when -7≤D < -4.5；
It 3) is high Threat when -10≤D < 7.
Civil aviaton's security public sentiment sentiment analysis method provided by the invention has the advantage that (1) present invention utilizes text language Justice and the mode that combines of microblogging emoticon determine the emotion score of microblogging text, overcome the office of dictionary and semantic rules It is sex-limited, improve the accuracy of emotion score judgement.(2) to the prestige of the microblogging text on the basis of microblog text affective score Stress score value is calculated, and obtains Threat grade, improves public security department, civil aviaton pre-alerting ability, has highly important meaning Justice.(3) the characteristics of making full use of microblogging text determines Threat more reasonable grade.(4) present invention is different from machine learning Method does not need to be trained with extensive tagged data, therefore is more suitable for real time data stream process.
Detailed description of the invention
Fig. 1 is civil aviaton's security public sentiment sentiment analysis method flow diagram provided by the invention.
Fig. 2 is emotion score value calculation method flow chart in the present invention.
Specific embodiment
Civil aviaton's security public sentiment sentiment analysis method provided by the invention is carried out in the following with reference to the drawings and specific embodiments detailed It describes in detail bright.
As shown in Figure 1, civil aviaton's security public sentiment sentiment analysis method provided by the invention includes the following step carried out in order It is rapid:
(1) behaviour is retrieved, pre-processed and segmented to the microblogging text on internet including civil aviaton's security public sentiment keyword Make；
Grab include on internet civil aviaton's security public sentiment keyword microblogging text, as analysis object of the invention, from Retrieval is related to the keyword of civil aviaton's security public sentiment in these microblogging texts, and keyword is divided into two class of place word and behavior word, To guarantee the comprehensive of data acquisition, search strategy is divided into single word retrieval and combined retrieval two ways, single word inspection Rope is individually retrieved two class words, wherein place word such as airport, runway, terminal, flight etc., behavior word Such as aircraft bombing, airplane hijacking, sky make a noise, have a fist fight, protest；Combined retrieval be place+behavior search modes, such as " airport+bomb ", " flight+explosion ", " airport+airplane hijacking " etc., and in the database by search result storage；
Meanwhile in order to improve system effectiveness, it is arranged microblog users " white list ", the microblog users in the list are various regions machine Field public security official's microblogging and news portal website microblogging.Since these microblog users are often issued comprising civil aviaton's security public sentiment key The microblogging of word, but not within the scope of early warning and monitoring, therefore removed in keyword retrieval.
Then pretreatment operation is carried out to above-mentioned search result, to remove noise unrelated with emotional expression in microblogging text Information, such as: (1) web page interlinkage, shaped like " http://t.cn/Rtj0WWN " etc., due to not including useful information, pre- It is removed when processing.(2) user's pet name when forwarding, reply microblogging, topic label, spcial character etc., shaped like " Li Qiongzi: time Multiple sketch craftsman Lao Wang: this thing return insane asylum pipe uncle police regardless of ", wherein the microblog users name needs after symbol are gone It removes.And emoticon is extracted, due to emoticon square brackets textual representation in the microblogging text that grabs, such as " here it is mouths The witness [strong] of upright stone tablet and effect ", emoticon therein isThe text in square brackets is extracted, the feelings for emoticon Inductance value calculates.
It is segmented later to above-mentioned by pretreated result using participle tool, participle tool is increased income using Java Participle tool Ansj.
(2) building is for all kinds of dictionaries needed for the analysis of microblogging text semantic, construction method be divided into choose existing dictionary and The composition of the mode independently constructed, all kinds of dictionaries is as follows:
1) sentiment dictionary: the sentiment dictionary of mainstream has Taiwan Univ.'s NTUSD dictionary, Hownet HowNet Chinese and English emotion at present Dictionary, Dalian University of Technology's emotion vocabulary ontology library etc..In these types of mainstream dictionary, Dalian University of Technology's emotion vocabulary ontology Word is divided into front, negative, neutrality three classes by library, and marks feeling polarities according to 1,3,5,7,9 five kind of intensity, is conducive to microblogging The calculating of text emotion value, therefore select the dictionary as the sentiment dictionary in the present invention.
It 2) negate dictionary: when occurring negative word before emotion word, it may occur that the reversion of feeling polarities, the present invention count Common negative word totally 19, thus it is configured to negative dictionary.The negative word are as follows: not, do not have, nothing, it is non-, not, not, not, not, It is no, other, be not nothing but, not enough, or not never, may not, not have, not be difficult to, not.
3) modify dictionary: according to the semantic rules of Chinese grammer, the power of emotional value and the modification of degree adverb have Direct relation, so it is very necessary that qualifier is chosen for one of judgment rule.Modification dictionary in the present invention selects Hownet (HowNet) the Chinese degree rank word in, is divided into 6 ranks, as shown in table 1.
Table 1 modifies dictionary example
4) conjunction dictionary: in compound sentence, conjunction will lead to the variation of feeling polarities, so that the judgement to feeling polarities produces It is raw to influence.The present invention has chosen the conjunction with turning relation, progressive relationship and concession relationship as conjunction dictionary.Described Indicate turning relation conjunction are as follows: still but however and can be but be exactly, be, can；Indicate the company of progressive relationship Word are as follows: and even, so that and especially；Indicate concession relationship conjunction are as follows: even if although even if although, even,.
5) emoticon dictionary: providing a large amount of emoticon in Sina weibo, the present invention has chosen in Sina weibo Expression is set, feeling polarities are divided into positively and negatively two class, and are manually marked according to intensity.As shown in table 2.
2 emoticon dictionary example of table
6) network hot word dictionary: microblogging as a kind of social media, text have the characteristics that it is unofficial and colloquial, because The frequency of use of this cyberspeak is very high, but these vogue words are not comprised in traditional sentiment dictionary, by network neologisms It is very necessary that sentiment dictionary is added.In net word net (http://wangci.net), there are network prevalence term dictionary and paraphrase. 494 network hot topic words that the present invention has grabbed the website mark out feeling polarities according to criterion identical with sentiment dictionary It is configured to network hot word dictionary with intensity, the supplement as sentiment dictionary.
7) civil aviaton's security public sentiment dictionary: the place word and behavior word chosen in civil aviaton's security public sentiment keyword are configured to Civil aviaton's security public sentiment dictionary.In addition to basic word, the present invention has collected the unisonance wrong word of common word to increase the complete of dictionary Property.For example, the unisonance wrong word " swindleness " of " fried ", the unisonance wrong word " cutting " of " misfortune " etc., in dictionary increased word be " swindleness machine ", " swindleness bullet ", " cutting machine " etc..
(3) dictionary constructed according to above-mentioned steps (2) is given a mark to the above-mentioned microblogging after step (1) participle, is obtained The emotion score value of the microblogging；
Specific steps are as shown in Figure 2.
1) it extracts from the above-mentioned microblogging text after step (1) participle or determines emotion word:
The word and above-mentioned sentiment dictionary and network hot word dictionary that obtain after participle in above-mentioned microblogging text are carried out Matching, if a certain word is present in above-mentioned two dictionary, is chosen for emotion word；
If word does not appear in sentiment dictionary and network hot word dictionary, determined with the method for semantic similarity Emotion word.In order to reduce operand, noun, verb and adjective alternately emotion word are only remained.The present invention utilizes Hownet Arithmetic of Semantic Similarity has good effect as benchmark algorithm on measuring two Words similarities.Specific method is For two word w1And w2If word w1There are the n senses of a dictionary entry or concept: x1,x2…,xn, word w2There are the m senses of a dictionary entry or concept: y1,y2…,ym, it is specified that word w1And w2Similarity be each senses of a dictionary entry or concept similarity maximum value, it may be assumed that
The former calculating formula of similarity of two justice are as follows:
Wherein, λ is positive variable element；d(x1,y2) indicate adopted original x1With adopted original y2Distance in hierarchical tree.
For any one word, can be obtained by calculating the similarity in the word and sentiment dictionary between seed words Its Sentiment orientation value, calculation method is: word w is carried out phase by formula (1) and formula (2) with seed words each in positive emotion dictionary The similarity of the word Yu front seed words is calculated like degree, then seed words each in word w and negative emotion dictionary are carried out Similarity calculation obtains the similarity of the word Yu negative seed words, by comparing the equal difference between them, finally obtains word w Sentiment orientation value, calculation formula is as follows:
Wherein, piIndicate a certain positive emotion seed words, njIndicate a certain negative emotion seed words；Sentiment orientation value Sw's Value range is (- 1,1).Given threshold T, by calculated Sentiment orientation value SwIt is compared with threshold value T, to determine word w Whether emotion word is belonged to.When | Sw| when > T, determine that word w is emotion word.The intensity of the emotion word is set to 10Sw, thus and feelings Level of intensity in sense dictionary is consistent.
2) the text emotion score of each microblogging clause in microblogging comprising above-mentioned emotion word is determined；
If 2.1) in microblogging clause include emotion word, and occur the negative word belonged in negative dictionary or modification before it When qualifier in dictionary, the text emotion score Sa of microblogging clause is calculated by following several situations:
I) degree adverb+emotion word, emotion word intensity change with adverbial word intensity, text emotion score are as follows:
Sa=Ma·ps·pa (4)
J) polarity of negative word+emotion word, emotion word changes, text emotion score according to the number of negative word are as follows:
Sa=(- 1)n·ps·pa (5)
K) degree adverb+negative word+emotion word, the reversion of emotion word polarity, and intensity changes with adverbial word intensity, text feelings Feel score are as follows:
Sa=(- 1) Ma·ps·pa (6)
L) negative word+degree adverb+emotion word, before appearing in degree adverb due to negative, after the reversion of emotion word polarity, Emotion word intensity more directly negates to be weakened, and introduces the first weight factor z1=0.5, text emotion score are as follows:
Sa=(- 1) Ma·ps·pa·z1 (7)
Wherein, ps indicates the intensity of emotion word, and pa indicates emotion word polarity, MaIndicate the intensity of degree adverb:
2.2) if comprising the adversative conjunction in conjunction dictionary in microblogging clause, microblogging clause belongs to compound sentence, it is contemplated that Feeling polarities transfer between sentence, the text emotion score of microblogging clause is calculated by following several situations:
G) turning relation: when occur in microblogging clause " still ", " however " etc. semantic reversion vocabulary when, previous microblogging clause Polarity will change, the integral polarity of the two microbloggings clause will be identical as the latter microblogging clause, introduce second power Repeated factor z2=-1, text emotion score are as follows:
Sen=z2Sen1+Sen2 (8)
H) progressive relationship: former and later two microbloggings clause's polarity is identical, enhanced strength, introduces third weight factor z3=1.5, Text emotion score are as follows:
Sen=z3(Sen1+Sen2) (9)
I) concession relationship: the polarity of the latter microblogging clause can invert, the polarity of whole sentence and previous microblogging clause phase Together, the 4th weight factor z is introduced4=-1, text emotion score are as follows:
Sen=Sen1+z4Sen2 (10)
Wherein, Sen1Indicate the text emotion score of previous microblogging clause, Sen2Indicate the text of the latter microblogging clause Emotion score；
3) emoticon score in microblogging is determined；
A large amount of emoticon is provided in Sina weibo, by using emoticon that can brightly indicate in microblogging The Sentiment orientation of the microblogging out.Using emoticon as a weighted term of emotion score value, for the emotion of whole microblogging text Tendency determines there is certain correcting action.According to emoticon dictionary, find in the microblogging polarity of all emoticons and Intensity, and record the number of each emoticon；Enable NiFor the number of i-th of emoticon, eiFor the intensity of the emoticon, piFor the polarity of the emoticon, then the emoticon score calculation formula in microblogging are as follows:
4) the text emotion score of above-mentioned microblogging and emoticon score are weighted summation, it is micro- that each can be obtained Rich emotion score value, formula are as follows:
S1=α scoreemo+β·scoretext (12)
Wherein, α, β are adjustable weight, and value range is (0,1), and alpha+beta=1 can be selected by the verifying of cross-beta collection α, β value when correct class probability maximum；scoretextIt is each microblogging clause text emotion for the text emotion score of the microblogging The average value of score.As emotion score value S1For timing, determine that the microblogging expresses positive emotion；As emotion score value S1When being negative, determine The microblogging expresses negative sense emotion.
(4) the emotion score value according to obtained in step (3) carries out subjective and objective differentiation to microblogging, exists for filtering news report Interior objective microblogging retains the microblogging for having subjectivity, finally obtains the microblogging to the Threat score value of safety of civil aviation；
Subjective and objective differentiation is carried out to microblogging text using following methods first:
1) for emotion score value S1=0 microblogging, if wherein including first person noun or pronoun, then it is assumed that be subjective micro- Otherwise blog article sheet is objective microblogging text.
2) for emotion score value S1≠ 0 microblogging, if the wherein special predicate word comprising news report or microblogging text In hop count at least 2 times, then it is assumed that be objective microblogging text, be otherwise subjective microblogging text.
The Threat score value of objective microblogging text is set as 0, and is calculated without following Threat score value, is only counted The Threat score value for calculating subjective microblogging, shown in calculation formula such as formula (13):
Wherein, D indicates Threat score value, and range is between [- 10,10]；S1Indicate the emotion score value of microblogging text；S2< w1,w2> is that civil aviaton's security public sentiment threatens score, w1Indicate place word, w2Expression behavior word；
In civil aviaton's security public sentiment dictionary, place word includes airport, runway, terminal, flight etc., and behavior word includes Aircraft bombing, airplane hijacking, sky make a noise, have a fist fight, protest；Wherein there are two attributes for behavior word, and first attribute is intensity, has measured the word Language is divided into 1,3,5,7,9 five kinds of intensity, the strength metric one with emotion word to the threat degree of civil aviaton's security, module It causes.Second attribute is type of word, and type of word is divided into two classes, and one kind is Direct-type, i.e., this word energy only occurs It is determined as there is threat to civil aviaton, such as aircraft bombing, airplane hijacking, despot's machine etc.；Another kind of is indirect-type, i.e., must go out simultaneously with place word Whether to civil aviaton security have threat, such as have a fist fight, protest, smoke if now can just determine.When only existing indirect-type behavior word, It is not enough to judge that it has threat to civil aviaton's security.
Civil aviaton's security public sentiment threatens score S2< w1,w2The calculating process of > is as follows: searching the behavior word in microblogging text w2, then judge the type of behavior word；When behavior word is Direct-type, civil aviaton's security public sentiment threatens score S2< w1, w2The value of > takes the intensity of behavior word；When behavior word is indirect-type, judge in the microblogging text whether and meanwhile deposit In place word, if existed simultaneously, civil aviaton's security public sentiment threatens score S2< w1,w2The value of > takes the strong of behavior word Degree threatens score S if do not existed simultaneously2< w1,w2> is 0.
(5) determine the speech in microblogging text to the Threat of safety of civil aviation according to the Threat score value that step (4) obtains Then grade filters out the high emphasis personnel of Threat grade, and reports and submits relevant departments as warning information.
The Threat score value obtained in step (4) can be seen that the microblogging text representation as Threat score value D > 0 Be positive emotion, belong to safe speech, therefore determine without Threat grade；As Threat score value D≤0, determining should Microblogging text contains civil aviaton's security public sentiment keyword, and what is expressed is Negative Affect, needs to pay close attention to, then according to following Threat classification standard determines microblogging text degree of impending grade.Threat classification standard be to existing microblogging text into It is specific as follows obtained from row test:
It 1) is low Threat when -4.5≤D≤0.
It 2) is medium Threat when -7≤D < -4.5.
It 3) is high Threat when -10≤D < 7.
Table 3 is listed certain microblogging texts are handled according to the method for the present invention after obtained Threat score value and threat Spend grade.As can be seen from the table, the method for the present invention can accurately determine whether microblogging text has safety of civil aviation It threatens.
The Threat of 3 microblogging text of table determines result

Claims (2)

1. a kind of civil aviaton's security public sentiment sentiment analysis method, civil aviaton's security public sentiment sentiment analysis method include in order into Capable the following steps:
(1) operation is retrieved, pre-processed and segmented to the microblogging text on internet including civil aviaton's security public sentiment keyword；
(2) building is for all kinds of dictionaries needed for the analysis of microblogging text semantic, and construction method, which is divided into, chooses existing dictionary and autonomous The mode of construction；
(3) dictionary constructed according to above-mentioned steps (2) gives a mark to the above-mentioned microblogging after step (1) participle, it is micro- to obtain this Rich emotion score value；
(4) the emotion score value according to obtained in step (3) carries out subjective and objective differentiation to microblogging, for including filtering news report Objective microblogging retains the microblogging for having subjectivity, finally obtains the microblogging to the Threat score value of safety of civil aviation；
(5) the Threat score value obtained according to step (4) determine the speech in microblogging text to the Threat grade of safety of civil aviation, Method is as Threat score value D > 0, which is positive emotion, belongs to safe speech, therefore without prestige Stress grade determines；As Threat score value D≤0, determine that the microblogging text contains civil aviaton's security public sentiment keyword, and express It is Negative Affect, needs to pay close attention to, then according to following Threat classification standard to microblogging text degree of impending grade Determine；Threat classification standard is obtained from testing existing microblogging text, specific as follows:
It 1) is low Threat when -4.5≤D≤0；
It 2) is medium Threat when -7≤D < -4.5；
It 3) is high Threat when -10≤D < 7；
Then the emphasis personnel with high Threat grade are filtered out, and as warning information；
It is characterized by: in step (3), the dictionary constructed according to above-mentioned steps (2), to above-mentioned through step (1) point Microblogging after word is given a mark, and the method for obtaining the emotion score value of the microblogging includes the following steps:
1) it extracts from the above-mentioned microblogging text after step (1) participle or determines emotion word:
The method for extracting emotion word is the word and above-mentioned sentiment dictionary and net that will be obtained after participle in above-mentioned microblogging text Network hot word dictionary is matched, if a certain word is present in above-mentioned two dictionary, is chosen for emotion word；
The method for determining emotion word is similar using semanteme with the word in network hot word dictionary to sentiment dictionary is not appeared in Degree method carries out；Specific method is for two word w1And w2If word w1There are the n senses of a dictionary entry or concept: x1,x2…,xn, word Language w2There are the m senses of a dictionary entry or concept: y1,y2…,ym, it is specified that word w1And w2Similarity be each senses of a dictionary entry or concept similarity most Big value, it may be assumed that
The former calculating formula of similarity of two justice are as follows:
Wherein, λ is positive variable element；d(x1,y2) indicate adopted original x1With adopted original y2Distance in hierarchical tree；
Each seed words in word w and positive emotion dictionary are subjected to similarity calculation by formula (1) and formula (2) and obtain the word and just The similarity of face seed words, then by seed words each in word w and negative emotion dictionary carry out similarity calculation obtain the word with The similarity of negative seed words finally obtains the Sentiment orientation value of word w by comparing the equal difference between them, calculates public Formula is as follows:
Wherein, piIndicate a certain positive emotion seed words, njIndicate a certain negative emotion seed words；Sentiment orientation value SwValue Range is (- 1,1)；Given threshold T, by calculated Sentiment orientation value SwIt is compared with threshold value T, whether to determine word w Belong to emotion word；When | Sw| when > T, determine that word w is emotion word, the intensity of the emotion word is set to 10Sw；
2) the text emotion score of each microblogging clause in microblogging comprising above-mentioned emotion word is determined；
If 2.1) in microblogging clause include emotion word, and occur the negative word belonged in negative dictionary or modification dictionary before it In qualifier when, the text emotion score Sa of microblogging clause is calculated by following several situations:
A) degree adverb+emotion word, emotion word intensity change with adverbial word intensity, text emotion score are as follows:
Sa=Ma·ps·pa (4)
B) polarity of negative word+emotion word, emotion word changes, text emotion score according to the number of negative word are as follows:
Sa=(- 1)n·ps·pa (5)
C) degree adverb+negative word+emotion word, the reversion of emotion word polarity, and intensity changes with adverbial word intensity, and text emotion obtains It is divided into:
Sa=(- 1) Ma·ps·pa (6)
D) negative word+degree adverb+emotion word, before appearing in degree adverb due to negative, after the reversion of emotion word polarity, emotion Word intensity more directly negates to be weakened, and introduces the first weight factor z1=0.5, text emotion score are as follows:
Sa=(- 1) Ma·ps·pa·z1 (7)
Wherein, ps indicates the intensity of emotion word, and pa indicates emotion word polarity, MaIndicate the intensity of degree adverb:
2.2) if comprising the adversative conjunction in conjunction dictionary in microblogging clause, microblogging clause belongs to compound sentence, it is contemplated that between sentence Feeling polarities transfer, the text emotion score of microblogging clause is calculated by following several situations:
A) turning relation: when occur in microblogging clause " still ", " however " including semantic reversion vocabulary when, previous microblogging clause Polarity will change, the integral polarity of the two microbloggings clause will be identical as the latter microblogging clause, introduce second power Repeated factor z2=-1, text emotion score are as follows:
Sen=z2Sen1+Sen2 (8)
B) progressive relationship: former and later two microbloggings clause's polarity is identical, enhanced strength, introduces third weight factor z3=1.5, text Emotion score are as follows:
Sen=z3(Sen1+Sen2) (9)
C) concession relationship: the polarity of the latter microblogging clause can invert, and the polarity of whole sentence is identical as previous microblogging clause, draw Enter the 4th weight factor z4=-1, text emotion score are as follows:
Sen=Sen1+z4Sen2 (10)
Wherein, Sen1Indicate the text emotion score of previous microblogging clause, Sen2Indicate the text emotion of the latter microblogging clause Score；
3) emoticon score in microblogging is determined；
According to emoticon dictionary, the polarity and intensity of all emoticons in the microblogging are found, and records each emoticon Number；Enable NiFor the number of i-th of emoticon, eiFor the intensity of the emoticon, piFor the polarity of the emoticon, then Emoticon score calculation formula in microblogging are as follows:
4) above-mentioned microblog text affective score and emoticon score are weighted summation, obtain the emotion of each microblogging Score value, formula are as follows:
S1=α scoreemo+β·scoretext (12)
Wherein, α, β are adjustable weight, and value range is (0,1), and alpha+beta=1 can be selected correct by the verifying of cross-beta collection α, β value when class probability maximum；scoretextIt is each microblogging clause text emotion score for the text emotion score of the microblogging Average value.
2. civil aviaton's security public sentiment sentiment analysis method according to claim 1, it is characterised in that: described in step (4) The emotion score value according to obtained in step (3) subjective and objective differentiation is carried out to microblogging, it is objective including news report for filtering Microblogging retains the microblogging for having subjectivity, and finally obtain the microblogging is to the method for the Threat score value of safety of civil aviation:
Subjective and objective differentiation is carried out to microblogging text using following methods first:
1) for emotion score value S1=0 microblogging, if wherein including first person noun or pronoun, then it is assumed that be subjective microblogging text This, is otherwise objective microblogging text；
2) for emotion score value S1≠ 0 microblogging, if wherein including in the special predicate word or microblogging text of news report Hop count at least 2 times, then it is assumed that be objective microblogging text, be otherwise subjective microblogging text；
The Threat score value of objective microblogging text is set as 0, and is calculated without Threat score value, subjective microblogging is only calculated Threat score value, shown in calculation formula such as formula (13):
Wherein, D indicates Threat score value, and range is between [- 10,10]；S1Indicate the emotion score value of microblogging text；S2< w1,w2 > is that civil aviaton's security public sentiment threatens score, w1Indicate place word, w2Expression behavior word；
Civil aviaton's security public sentiment threatens score S2< w1,w2The calculating process of > is as follows: searching the behavior word w in microblogging text2, so The type of behavior word is judged afterwards；When behavior word is Direct-type, civil aviaton's security public sentiment threatens score S2< w1,w2> Value take the intensity of behavior word；When behavior word is indirect-type, judge whether exist simultaneously ground in the microblogging text Point word, if existed simultaneously, civil aviaton's security public sentiment threatens score S2< w1,w2The value of > takes the intensity of behavior word, such as Fruit does not exist simultaneously, and threatens score S2< w1,w2> is 0.
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CN113220962A (en) * 2020-09-10 2021-08-06 深圳信息职业技术学院 Public opinion analysis method based on internet big data
CN112016331A (en) * 2020-10-30 2020-12-01 成都智元汇信息技术股份有限公司 Passenger transport passenger emotion analysis method
CN112417258A (en) * 2020-12-02 2021-02-26 深圳市罗湖医院集团 Crushing method, platform and terminal for rumor information in health knowledge search engine
CN112364947B (en) * 2021-01-14 2021-06-29 北京育学园健康管理中心有限公司 Text similarity calculation method and device
CN117010409B (en) * 2023-10-07 2023-12-12 成都中轨轨道设备有限公司 Text recognition method and system based on natural language semantic analysis

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101894102A (en) * 2010-07-16 2010-11-24 浙江工商大学 Method and device for analyzing emotion tendentiousness of subjective text
US8165869B2 (en) * 2007-12-10 2012-04-24 International Business Machines Corporation Learning word segmentation from non-white space languages corpora
CN103207860A (en) * 2012-01-11 2013-07-17 北大方正集团有限公司 Method and device for extracting entity relationships of public sentiment events
CN103530360A (en) * 2013-10-12 2014-01-22 广西师范学院 Network society influence maximization algorithm based on microblog text affective computing
CN103559233A (en) * 2012-10-29 2014-02-05 中国人民解放军国防科学技术大学 Extraction method for network new words in microblogs and microblog emotion analysis method and system
CN104516962A (en) * 2014-12-18 2015-04-15 北京牡丹电子集团有限责任公司数字电视技术中心 Monitoring method and system for microblogging public opinion
CN104537097A (en) * 2015-01-09 2015-04-22 成都布林特信息技术有限公司 Microblog public opinion monitoring system
CN104809104A (en) * 2015-05-11 2015-07-29 苏州大学 Method and system for identifying micro-blog textual emotion
CN105389389A (en) * 2015-12-10 2016-03-09 安徽博约信息科技有限责任公司 Network public opinion transmission situation media linked analysis method

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8165869B2 (en) * 2007-12-10 2012-04-24 International Business Machines Corporation Learning word segmentation from non-white space languages corpora
CN101894102A (en) * 2010-07-16 2010-11-24 浙江工商大学 Method and device for analyzing emotion tendentiousness of subjective text
CN103207860A (en) * 2012-01-11 2013-07-17 北大方正集团有限公司 Method and device for extracting entity relationships of public sentiment events
CN103559233A (en) * 2012-10-29 2014-02-05 中国人民解放军国防科学技术大学 Extraction method for network new words in microblogs and microblog emotion analysis method and system
CN103530360A (en) * 2013-10-12 2014-01-22 广西师范学院 Network society influence maximization algorithm based on microblog text affective computing
CN104516962A (en) * 2014-12-18 2015-04-15 北京牡丹电子集团有限责任公司数字电视技术中心 Monitoring method and system for microblogging public opinion
CN104537097A (en) * 2015-01-09 2015-04-22 成都布林特信息技术有限公司 Microblog public opinion monitoring system
CN104809104A (en) * 2015-05-11 2015-07-29 苏州大学 Method and system for identifying micro-blog textual emotion
CN105389389A (en) * 2015-12-10 2016-03-09 安徽博约信息科技有限责任公司 Network public opinion transmission situation media linked analysis method

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