文本的情感倾向分析方法
The Method of Analyzing Affective Tendency in Text
Song Guangpeng
CISTR，BUPT，Beijing （100876）
E-mail：https://www.360docs.net/doc/c17942160.html,@https://www.360docs.net/doc/c17942160.html,
Abstract
This paper introduces a method targeted at analyzing affective tendency of Chinese texts. First of all, Chinese texts are processed, and then affective words in texts are tagged with the affective words dictionary, and then sentence structure are analyzed. Affective values of the various elements of the sentences have different affective effect to the affective values of sentences; hence all affective elements for the affective values of sentences should be analyzed and weighted. The affective dictionary is based on psychology model, each word has two affective dimensions: activation value, pleasure value. Each word in every dimension has a corresponding value. The affective value of the text is two-dimensional. An affective tendency analyze system targeted at Chinese texts is realized, which consists of a Chinese processing engine and an affective analyzing engine. The affective tendency engine includes affective words identification function, and a rule set of sentences structure. Tests were carried out by using the affective texts.
Keywords：affective computing，affective model
1Introduction
Analyzing the contents of the text, and extracting the affective elements, cause the researchers’ interest more and more. Through the exchange of language, the content of communication is not only information, but also affective element.
There has been a recent swell of interest in the automatic identification and extraction of opinions, emotions in text. Motivation for this task comes from the desire to provide tools for information analysts in government, business, political domains, for automatic tracking attitudes and feelings in the news and forum. How do people feel about Spider-man 3? Whether the recent stock market overheating? Whether the prices of house are beyond the purchasing power of the people? An emotional automatic identification system is capable of answering the questions very helpful.
Currently, there are several methods of Affective Computing for texts: keyword spotting, lexical affinity, statistical methods, hand-crafted models, and using large-scale real-world knowledge about the inherent affective nature of everyday situations to classify sentences into “basic” emotion categories.
Each method has own advantages and weaknesses, and there is not a perfect method. The method to analyze the affective tendency in text is based on keyword spotting, and the structure of sentence is analyzed. For different structure, elements in the text have different impact on the affective tendencies of text. The affective tendency of text is determined by analyzing every affective element in the text.
This paper is organized as follow. In section 2 and 3, the background knowledge is reviewed, to be briefed about the concept of affective computing, emotional model. In section 4, the test strategy for the engine is given. Finally in section 5, conclusion is given.
2Affective computing
Professor Picard of M.I.T Media Laboratory published “Affective Computing” in 1997 [1]. In the book, she defined “Affective Computing” is computing that relates to, arise from, or deliberately influences emotion or other affective phenomena.
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Affective computing is aiming to give computer ability of recognition, understanding, expressing and adapting to the capacity of human emotion, to build a harmonious human-machine environment, and more comprehensive intelligent.
Human’s communication and exchange is natural and full of feelings, therefore, in the process of human-computer interaction people expect that computer will have affective capacity. Affective Computing, which give the computer ability of observing and understanding, producing all emotional characteristics, finally, make the computer can be natural, cordial and lively communicate like human.
Affective Computing will effectively change the rigid human-computer communication, and improve the human-computer interaction in a cordial and accuracy. The computer that has a capacity of affective computing can acquire, classify, identify and response to human’s emotion and help users get efficient and cordial feeling, and effectively reduce frustrations of the computer user, even help people understand their own and other people's affective world.
It can also help us to increase safety of using equipment and humanization of experience, and make computers as a medium for learning function to the best, and collecting information from the feedback. For example, a research project using the vehicle computer to measure the pressure level of motorists, help researchers resolve the problem so-called "road raging disease."
Affective computing and related research is also able to bring benefits to e-commerce enterprises. Research has shown that different images can arouse different human emotions. For example, the pictures of snakes, spiders and guns, can arouse fear, and the pictures of a large amount of cash and bullion are able to make people a very strong positive response. When websites of shopping and stock dealing are designed, if considering signification of these factors, it will have a very positive impact to the amount of net-surfers.
3Experiment Results and Analysis
Figure 1 depicts the essence of Watson and Tellegen’s Circumplex Theory of Affect.
In their research on the structure of affect, Watson and Tellegen consistently encountered the same two major bipolar dimensions: positive affect and negative affect. Positive affect reflects a combination of high energy and positive evaluation characterized in such emotions as elation. Negative affect comprises feelings of upset and distress (Watson and Tellegen 1985) [2]. Both positive and negative affects occur on bipolar continua, ranging from high to low. Note, however, that many affectively loaded words are not pure markers of either factor (see Figure 1).
The pleasantness octant contains terms representing a mixture of high positive and low negative affect (e.g. pleased, satisfied). Unpleasantness includes combinations of high negative and low positive affect (e.g. blue, grouchy). Pleasantness-unpleasantness and strong-engagement-disengagement axes form an alternative orientation on the circumplex (imagine the figure turned clockwise by 45 degrees). Inclusion of two alternative rotations gives the map a circular appearance [3].
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HIGH POSITIVE AFFECT
LOW POSITIVE AFFECT
Figure 1: Watson and Tellegen's two-dimensional map
When the emotional dictionary is constructed, the affective words are two-dimensional tagged. Unlike the
one-dimensional classification of affective words, two-dimensional tagging can describe the affective
words more delicate. Many affective words, such as "fear", "surprised", and "quiet", are not described very
well in one-dimensional. They are interaction of several affective dimensions. Therefore, the affective
words in two-dimensional tagged can enrich the connotation of affective words. The affective dictionary
was tagged in active dimension and pleasure dimension. Each dimension was dived into nine sections, As
Table 1 below
Table 1: Affective Word Tagged
Affective Word Activation Value Pleasure Value
①高兴0 4
②惊讶 3 0
③悲伤0 -3
④安静-2 0
⑤缓慢-3 -3
⑥恐惧 2 -2
⑦葬礼 2 -4
⑧表扬0 3 4System Construction
Chinese Processing:
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Fig 2. NLU Model
As shown in Figure 2, Chinese processing module performs Chinese segmentation, POS tagging and affective tagging.
We select the ICTCLAS for the segmentation module. ICTCLAS apply to word segmentation class-based HMM. Given a word Wi class Ci is defined. Suppose [LEX] to be the lexicon size, then the total number of word classes is [LEX]+9.
This Chinese lexical analysis is based on Shai's work[3] given a formal description of HHMM. For convenience, they also use the negative log probability instead of the proper form.
That is:
[]#
11arg min ln (|)ln (|)m
i i i i W i W p w c p c c ?==??∑ According to the word class definition, if Wi is listed in lexicon, then Ci is Wi , and p( Wi|Ci ) is equal to 1.0. Otherwise, p( Wi|Ci ) is probability that class Ci initially activates Wi, and it could be estimated in its child HMM for unknown words recognition [4].
Table 2: Segment and POS tagging Chinese text
(before processing)
虽然我通过了考试，但是我不高兴。 After segmentation
虽然/c 我/r 通过/v 了/u 考试/vn ，/w 但是/c 我/r 不/d 高兴/a 。/w Affective word
tagging 高兴（活跃度0愉悦度4）
Chinese text
(before processing)
著名演员马季因病去世，人们参加了他的葬礼。 After segmentation
著名/a 演员/n 马/nr 季/nr 因/p 病/n 去世/v ，/w 人们/n 参加/v 了/u 他/r 的/u 葬礼/n 。/w Affective word tagging 去世（活跃度0愉悦度-4）
葬礼（活跃度2愉悦度-4）
Sentence Structure Analyzing ：
Fig 3. Sentence Structure Analyzing Model
As shown in Figure 3, sentence structure analysis module, was composed of sentence pattern recognition, conditional conjunction recognition, and adjunct of affective words recognition [5].
Sentence pattern analysis: affirmative sentence, such as ”我今天很高兴.” (I am very happy today) Negative sentence, such as “我不高兴” (I am not happy)
Interrogative sentence, such as “他生气了吗?” (Is he angry?)
If there are negative words in front of the emotional words, the value of the sentence is negative. Conditional conjunction recognition:
If conditional verbs are found, the sentences do not express current feeling and will be disregarded by the engine.
Adjunct of affective words recognition:
If there are adjunct of affective word, the value of the sentence will be increased.
5Conclusion
From the investigation in this paper, it is proved that there are two contributions in our study: (1) the emotion thesaurus is constructed and can be solely used to obtain the emotion value and category of a single word; (2) the emotion recognition system set up in this paper can analyze the textual input including sentence and paragraph to obtain the emotion information such as the emotion value and category of emotion. The discussion in this paper shows that emotion information can be obtained by using the characteristic of natural language. The collection of language database is an important basic work for the emotion knowledge dictionary construction. In our study, several simple Chinese sentences are used to test the emotion recognition model. Finally, this model is improved to an effective and feasible tool to emotionally analyzing the textual input. It could be used for the language application in the future.
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References
Affective
Computing, The MIT Press, Mass., 1997.
W.,
R.
[1] Picard,
[2] Tellegen, A., Watson, D., Clark, L.A., 1999. On the dimensional and Hierarchical Structure of Affect. Psychological Science, V ol. 10, No 4, 297-303.
[3] Xiaoxi Huang, Y un Yang, Changle Zhou: Emotional Metaphors for Emotion Recognition in Chinese Text. ACII 2005: 319-325.
[4] Ying,Y.,Zhou,F.and Zhou. C.L.: A Research on Emotion Tagging of Chinese Understanding by Designing an Experiment System. Journal of Chinese Information Processing(2002).16(2): p.27-33
[5] Kazuyuki Matsumoto, Junko Minato, Fuji Ren, Shingo Kuroiwa, "Estimating Human Emotions Using Wording and Sentence Patterns," Proceedings of the 2005 IEEE, International Conference on Information Acquisition, June 27 - July 3, 2005, Hong Kong and Macau, China
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