Emotion Recognition from Blog Articles

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Nov 18, 2009 - computer can recognize the emotions expressed in blog ... the development of information processing technologies have greatly changed the ...

Emotion Recognition from Blog Articles Ji LI

Fuji REN

Dept. of Information Science and Intelligent System

Dept. of Information Science and Intelligent System

Faculty of Engineering

Faculty of Engineering

The University of Tokushima

The University of Tokushima

Tokushima, 770-8506, Japan

Tokushima, 770-8506, Japan

[email protected]

[email protected]

Abstract: In recent years, suicide of college students has been a universal phenomenon in the world. And the phenomenon has become more and more sever because of the complex and drastic competitions. With the popularization of internet and the development of information processing technologies, a lot of people have established their own blog websites to write down their experiences and express their feelings at times. It will be very helpful if the computer can recognize the emotions expressed in blog pages automatically. And then it will be convenient for teachers or psychological consultants to monitor the affective information of college students and take measures for the depression prevention when necessary. Owing to the advances in Affective Computing and Natural Language Processing, researches have begun to pay more attention to the emotion recognition in NLP all over the world. This paper outlines the approach we have developed to construct a Blog Emotion-Recognizing System. It is based on the lexical contents of words and structural characteristics of blog articles. For the emotion computing of articles, two methods are proposed and the experimental results are compared and analyzed. Finally, the implications of the results are discussed for the future’s direction of the research.

Keywords: Suicide; depression; blog; affective computing; natural language processing; emotion classification; structural characteristics; emotion recognition

1.

Introduction

In recent years, suicide of college students has been a universal phenomenon all over the world. Furthermore, the number of college students who suicide themselves has been increasing with amazing speed in a worldwide scope, around 100 million persons every year. In the year of 2003, World Health Organization (WHO) appointed September 10th as “World Suicide Prevention Day (WSPD)” to give a caution to the society. And it has adopted many suicide 978-1-4244-2780-2/08/$25.00 ©2008 IEEE

prevention programs and projects to avoid unnecessary death. At the same time, the problem of mental health among college students has raised great attention from more and more psychologists, educators and people in all the other professions. In order to intervene in the crisis, psychological consultation centers have been set up almost in every university or college [1]. However, some college students are afraid to talk about their private experiences with other people face to face, and refuse to consult psychological consultants when they meet troubles. In late decades, the popularization of internet and the development of information processing technologies have greatly changed the communication ways of mankind. More and more people have established their own blog websites over the internet where they write down their experiences, put forward their opinions, and express their feelings at times. While some people feel embarrassed to confide their troubles to others face to face, they may feel free to express their emotions in blogs without any pressures. It can be anticipated that in the near future, almost everyone in the world especially every college student will possess his or her own blog website. At that time, it will be very helpful if the computer can recognize the emotions expressed in blog articles automatically. In this case, a Blog Emotion-Recognizing System is needed. If the system detects that some college student has been in a blue mood for continuous days (for example, for continuous seven days), it is recommended that the teacher should pay more attention lately and have a talk with him or her regularly so that the depression can be treated in time. That’s why this research has been taken on. In this paper, we outline a new approach to recognize the author’s emotion from his or her blog articles. Based on the approach, a Blog Emotion-Recognizing System has been constructed. The results of our experiments on blogs prove the feasibility and effectivity of the means. The remaining part of the paper is organized as follows: Section 2 talks about present status in the fields of

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Affective Computing and Natural Language Processing. Section 3 describes the classification of emotion for vocabularies in the emotion dictionary. Section 4 illustrates the model of the Blog Emotion-Recognizing System in detail. Section 5 analyzes the structural characteristics of blog articles. Section 6 proposes two methods for emotion computing. Section 7 gives an evaluation and discussion on the experimental results of testing. Section 8 comes to a conclusion of the work done with some directions for future work.

communication mediums. Owing to the popularization of Internet and the development of studies on affective computing, researches have begun to pay more attention to the emotion recognition in NLP especially in America and Japan[8] [9] [10]. In these years, Chinese NLP has also become one of the most attractive focuses in the field of artificial intelligence [11] [12]. In our Blog Emotion-Recognizing System, the NLP technologies are certainly of great use for the emotion recognition from blog articles.

2.

It is not a trivial task to extract emotional information from the lexical content or meaning of the words in a blog. The fundamental component of the Blog Emotion-Recognizing System is the emotional dictionary through which the emotional category of a vocabulary can be looked up. And the emotion classification has a direct influence on the efficiency and accuracy of the emotion sensing. However, there is no standard for the classification of emotions. Researchers grouped affective words into categories defined by their own for specific study or use. In the past, emotion has been simply divided into two categories “pleasure” and ”displeasure” which are too ambiguous to assess rich emotions of human. Ekman has defined 7 universal affective categories [13] based on unique facial expressions which seem still less while applied into a practical system. In contemporary Chinese, 39 emotional categories are specified for vocabulary [14], nevertheless part of which is seldom used in daily communications.

Affective computing and natural language processing

With the progress in human-computer interface technology and the involvement of computer applications in our daily life, it becomes more and more important that human can communicate with computer in a natural way. Recognizing human's emotional states can be very helpful to smooth the interaction between human and computer. One of the famous researchers, Picard brought forward the concept of “affective computing”[2] at first. After that, machine emotional intelligence has drawn great attention from scientists and researchers not only in the area of artificial intelligence, but also in psychology and others [3]. They have devoted to extracting human’s affective information in many ways such as text, voice, facial expressions, gestures and even some psychological phenomena like galvanic skin response, temperature, heart rate, perspiration rate and etc [4] [5]. At the same time, recent technological advances have allowed to develop better applications of human-computer interface to assess human’s emotional states [6] [7]. One of the representative applications is the emotion-recognizing system used in a teleconference, which provides real-time information on the emotional states of the participants. Another example is an automatic tutoring application. It can automatically adjust the content and the speed of the tutorial depending on whether the learner feels bored or excited. Among all the communication mediums (like text, voice, facial expressions, and gestures), we should not forget that language and text play a very basic and important role in expressing our emotions during daily communications, and textual information is easily accessed in pervasive electronic documents like emails, web pages, electronic newspaper and so on. With the help of Natural Language Processing (NLP) technologies, emotional states under the text can be assessed. And in turn, the emotion recognition in text will enhance the emotion sensing from other

3.

Emotion classification for words

Table 1. Emotion vocabularies Emotional category

vocabulary

Happy

Sad

Fearful

Disgusted

快乐

哀愁

恐慌

嫌弃

幸福

痛心

胆怯

粗俗

舒坦

绝望

恐怖

反感

成功

疼痛

害怕

顽固

扬眉吐气

心如刀割

心惊胆战

味同嚼蜡

……

……

……

……

In our Blog Emotion-Recognizing System, we classified emotion based on the affective categories defined by Ekman plus some important categories found in blogs with high frequency of use. They are totally 26 categories: happy, sad, fearful, disgusted, angry, surprised, love, expectant, nervous, regretful, praiseful, shy, respectful, proud, impatient, doubtful, hateful, grievance, critical, depressed, exited, thankful, annoyed, scornful, haughty, envious. When there is no emotion expressed in a blog, we named the mental state

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as “neutral”. Some examples of emotional vocabularies are listed in Table 1. 4.

Model of blog emotion-recognizing system

The Blog Emotion-Recognizing System is composed of five modules: HTTP Downloading, HTML Parser, Morphological Analyzing, Emotion Tagging, Emotion Computing. The framework of the model is illustrated by Figure 1. In the following we will introduce the flow of how the system works step by step. Internet

Blog URL

HTTP Downloading

Blog webpage HTML Parser

lexical characteristics of the sentence and extract words from it based on the rule database according to analyzer rules. Here we select the ICTCLAS (Institute of Computing Technology, Chinese Lexical Analysis System) system for this module for its high segment accuracy in Chinese natural language. And then in the Emotion Tagging module, Emotion Dictionary is used to search for the emotion category of a word. In our system, we only check words with certain part of speech as keywords in the sentence. They are the adjective, adverb, verb, idiom and habitual words which are rich in emotions. Totally 7393 words are included in the Emotion Dictionary, collecting from People’s Daily tagging corpus and other dictionaries such as Xiandaihanyu Dictionary. The output of the step is the emotion category of each sentence. Finally, with the emotion category of each sentence, the Emotion Computing module will compute the weight value of emotion categories of all sentences in a blog article according to the blog structure rules, and output the emotion result for the whole article. In our system, we have tried two methods to realize this module which will be analyzed in detail in the later part of the paper. 5.

NL Sentence Morphological Analyzing

Analyzer Rules

Rule Database

Word Emotion Tagging

Emotion Dictionary

Emotion Category Emotion Computing (Method 1, 2)

Structure Rules

Emotion Result

Figure 1. Framework of the blog emotion-recognizing system Firstly, the System will download the blog webpage through Internet according to the web address (URL) of the blog which is inputted from keyboard. Secondly, the HTML Parser module analyzes the blog webpage that has been downloaded, and extracts each blog article in the webpage. Then it divides the blog article into paragraphs and sentences which are stored for further processing. Next, for each natural language sentence, the Morphological Analyzing module will analyze the

Analysis of blog articles

From the emotion sensing of sentences to the emotion recognition of an article, there is still a complex way to go. With only the emotion category based on the lexical information of words, the emotion expressed by the whole article can not be assessed correctly. In order to achieve the aim, the information of structural characteristics [15] [16] of blog articles must be acquired. Here it is assumed that there exits a central sentence which expresses the emotion of the whole article, and the central sentence is likely to locate in a certain place in the article with high probability. Then we choose 40 most recent articles of two blog authors with 20 articles for each. The blog authors are selected randomly under the blog community in some university. Next, we mark the central sentence expressing the emotion of each article manually, and assess the emotion into one of the 26 categories mentioned in Section 3. Table 2 demonstrates the most probable distributions of the central sentence throughout the articles. Here are some abbreviated symbols used in the table: PCS: The paragraph where the central sentence is located. NTP: The number of total paragraphs. LCS: The location of the central sentence. NTS: The number of total sentences in PCS. E: Emotion evaluated by human.

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Table 2. The distribution of the central sentence in blog articles No.

PCS

NTP

LCS

NTS

E

1

7

7

1

1

angry

2

title

hateful

3

title

depressed

4

title

5

4

4

7

4

8

8

1

4

6

3

scornful 3

thankful

1

4

depressed

4

4

depressed

title

9

annoyed

title

annoyed

10

5

5

6

6

happy

11

6

8

2

2

praiseful

12

6

6

3

3

Neutral

13

7

7

3

4

thankful

14

8

8

1

1

happy

15

2

7

1

9

depressed

16

1

2

1

3

17

title

18 19

title 7

7

21

9

10

22

1

9

23

6

9

20

1

annoyed 1

critical

1

1

depressed

1

2

happy

2

2

envious

title

24

love happy

depressed

title

25

sad

title

love

26

4

6

2

2

love

27

1

6

1

1

neutral

28

5

6

1

2

annoyed

29

title

annoyed

30

1

8

1

2

sad

31

2

4

1

2

happy

32

15

15

1

1

praiseful

33

1

11

1

1

depressed

34 35

Neutral 3

5

1

36

title

37

title

38

1

11

39

17

18

40

1

sad regretful love

1

1

depressed

1

1

annoyed

title

annoyed

Table 3. Structural weight values in blog articles Central Sentence in an Article title

Number of Occurrence 14

Weight 0.35

the last paragraph

8

0.2

the first paragraph

7

0.175

From the distribution of the central sentence in Table 2, we obtain the structural weight values for a blog article as showed in Table 3. We can see from Table 2 that the central sentence which expresses the emotion of the whole article tends to locate in three places mainly: the title, the last paragraph, and the first paragraph in a decreasing order of weight. The central sentence also appears in other places like the last second paragraph, the second paragraph, and the last third paragraph, and etc. It is of more probability for the central sentence to be in the paragraphs near the two ends of an article than the paragraphs near the middle. But there are no obvious rules to follow for the occurrence in these places. It can only be discovered that while the central sentence is located in the last paragraph, the last sentence in the paragraph is the central sentence in most cases; and while the central sentence appears in the first paragraph, the first sentence in the paragraph is usually the central sentence. For other paragraphs, last sentences in last paragraphs are more important than the ones from the beginning; and the sentences at the beginning in first paragraphs are more important than the ones from the end. 6.

Basic idea of two methods

From the analysis of blog articles in Section 5, we acquired the structural characteristics of blog articles. Making use of the structural information, two different methods are tried to compute the emotion in order to get better results. The critical problem in the emotion computing is how to find out the main emotion among all the emotions extracted from all the paragraphs and sentences in a blog article. 6.1.

Method 1

The basic idea of Method 1 is to add all the emotion weight values obtained from all the sentences, and the emotion category with the biggest weight value is the main emotion of a blog article. Let us suppose that there are i (i=0,1,2,…n) paragraphs in a blog article (here i=0 denotes the title of the article) and j (j=1,2,3, …m) sentences in each paragraph (m is variable for different paragraphs). For each sentence j in each paragraph i, there is a emotion weight value Wij assigned. When i=0, the weight Wij is 0.35 as listed in Table 3. When i=1, it denotes the first paragraph, and the weight Wij is 0.175. When i=n, it denotes the last paragraph, and the weight Wij is 0.2. For other paragraphs, the weight is averagely assigned as (1-0.175-0.2) / (n-3). Each sentence j in a paragraph i corresponds to a pair (Ek, Wij). Ek is one of the 26 emotion categories (k=1,2,…26). Ek with the maximum sum of all the

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Table 4. The result of experiment-evaluation on close test

corresponding Wij (i=0,1,2,…n and j=1,2,3, …m) is the emotion of the whole article. 6.2.

Method 2

The basic idea of Method 2 is to try a direct way to find out the main emotion of a blog article by searching for the central sentence according to the proportion of distribution in the article. We check for the main emotion in the order of title, the last paragraph, the first paragraph, the last second paragraph, the first second paragraph and so on. This is for the reason analyzed in Section 5 that the central sentence tends to appear in the paragraphs near the two ends of an article than the paragraphs near the middle. For last paragraphs, the searching is from the end of the paragraph to the beginning. For the first paragraphs, the searching order is from the beginning to the end. Once the emotion which appears first is extracted in the searching, it is the main emotion of a blog article. The emotion weight for each paragraph is the same as in Method 1. 7.

Experiments and evaluation

It is still an issue for the evaluation. By now there is not a good evaluation method generally accepted. Since different people may have different opinions even on the same text, it is common for them to give different evaluations of emotion manually. In our experiments, we carried on the close test and open test based on the manual evaluation of emotions as a standard set which is judged by evaluators in advance. 7.1.

Close test

The close test is carried out on 40 blog articles of two blog authors which are used to obtain the structural characteristics in Section 5. Two algorithms (Method 1 and 2) of emotion computing are realized in our system. And the evaluation is to count the number of correct prediction by the system compared with the standard set. Table 4 gives a list of all the evaluation results on the close test. The data in the table with the background of grey color is the incorrect prediction of emotion according to the standard set. And the percentages of correct prediction by two methods are listed in Table 5 respectively. Here are some abbreviated symbols used in Table 4: E: Emotion evaluated by human. ME 1: The machine emotion evaluated by the Blog Emotion-Recognizing System using Method 1. ME 2: The machine emotion evaluated by the Blog Emotion-Recognizing System using Method 2.

No.

E

1

angry

2

hateful

3

depressed

4

scornful

5

thankful

6

annoyed

7

depressed

8

depressed

9

annoyed

10

happy

11

praiseful

12

Neutral

13

thankful

14

happy

15

depressed

16

love

17

happy

18

annoyed

19

critical

20

depressed

21

depressed

22

happy

23

envious

24

sad

25

love

26

love

27

neutral

28

annoyed

29

annoyed

30

sad

31

happy

32

praiseful

33

depressed

ME 1 angry,0.2; grievance,0.175; love,0.138 hateful,0.931; critical,0.175; sad,0.069 depressed,0.868; sad,0.136; happy,0.039 scornful,0.35; praiseful,0.061 love,0.513; thankful,0.375; praiseful,0.138 annoyed,0.725; praiseful,0.275; love,0.267 depressed,0.091; happy,0.046 depressed,0.45; praiseful,0.175 annoyed,0.35; sad,0.11; impatient,0.11 praiseful,0.267; happy,0.183 praiseful,0.138; regretful,0.091; happy,0.045 sad,0.069; love,0.069; expectant,0.069 thankful,0.2; happy,0.055; sad,0.055 happy,0.246; angry,0.046; love,0.046 depressed,0.206; regretful,0.175; sad,0.138 neutral,0 happy,0.908;praiseful,0.09 2 impatient,0.092 annoyed,0.35 critical,0.375; sad,0.138; angry,0.138 depressed,0.745; praiseful,0.65; sad,0.11 fearful,0.034; depressed,0.034; haughty,0.034 happy,0.175; sad,0.118; praiseful,0.039 envious,0.039

ME 2 angry,0.2 hateful,0.35 depressed, 0.35 scornful,0.35 thankful,0.2 annoyed, 0.35 happy,0.046 praiseful, 0.175 annoyed,0.35 praiseful, 0.175 praiseful, 0.046 expectant, 0.069 thankful,0.2 happy,0.2 regretful, 0.175 neutral,0 happy,0.2 annoyed,0.35 critical,0.2 depressed, 0.35 depressed, 0.034 happy,0.175 envious,0.039

sad,0.2

sad,0.2

love,0.455; angry,0.055; hateful,0.055 love,0.206;depressed,0.131 happy,0.069 sad,0.069; praiseful,0.069

love,0.2

annoyed, 0.069 annoyed,0.525 sad,0.221; envious,0.045

depressed,0.2 sad,0.069 annoyed, 0.069 annoyed,0.35 sad,0.175

happy,0.313

happy,0.175

praiseful,0.29; depressed,0.175 depressed,0.375; love,0.2; fearful,0.91

praiseful,0.2

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love,0.2

34

praiseful,0.175; surprised,0.138 sad, 0.275

Neutral

35

sad

36

regretful

37

love

38

depressed

39

annoyed

40

annoyed

regretful,0.35 love,0.405; happy,0.2; depressed,0.175 depressed,0.206; sad,0.061; disgusted,0.061 annoyed,0.037; sad,0.018; depressed,0.018 annoyed,0.35

love

praiseful, 0.175 sad,0.275

14

regretful,0.35

15

love,0.35 depressed, 0.175 annoyed,0.02 annoyed,0.35

Table 5. The percentage of the close test

thankful love

17

happy

18

love

19

praiseful

20

Percentage of Correct Prediction (%) 87.5

21

1

Number of Correct Prediction 35

2

9

31

77.5

23

Method

love

16

Number of Incorrect Prediction 5

sad

Open test

sad expectant

Table 6. The result of experiment-evaluation on open test No.

ME 2 respectful,0.2

depressed critical

ME 1 respectful,0.2; critical,0.092; love,0.031 critical,1.55; happy,0.55; regretful,0.2 critical,0.183; disgusted,0.046; praiseful,0.046 critical,0.115; praiseful,0.023 annoyed,0.2;praiseful,0.09 1 happy,0.061 angry,0.421;disgusted,0.91; praiseful,0.045 Neutral,0 critical,0.35; sad,0.039

praiseful

praiseful,0.157

10

annoyed

annoyed,0.35; happy,0.031

praiseful, 0.039 annoyed,0.35

11

expectant

happy,1.675

surprised

surprised,0.35; praiseful,0.35;love,0.138 happy,0.275; love,0,275; expectant,0.2

1 2

E respectful critical critical

3 4 5 6 7 8 9

12 13

critical annoyed angry

happy

sad

25

The open test is carried out on 40 articles of other blog authors (different from the two blog authors in the close test). These blog articles are new ones that are not used in acquiring the structure information. Similarly, two methods of emotion computing are tried in the test in the same way as in the close test. Table 6 demonstrates all the evaluation results. The data in the table with the background of grey color is the incorrect prediction of emotion according to the standard set. And the percentages of correct prediction by two methods are listed in Table 7 respectively.

happy,0.35 critical,0.046

sad,0.275; love,0.23; expectant,0.2 happy,0.888; love,0.313; sad,0.2 love,0.22; expectant,0.175; thankful,0.175 praiseful,1.244; critical,0.068 happy,1.063; praiseful,0.35; annoyed,0.138 critical,0.525;hateful,0.175 ; praiseful,0.13 love,0.405; disgusted,0.2; critical,0.055 love,0.313; expectant,0.2; praiseful,0.2 love,0.55; sad,0.092; angry,0.092 love,0.22; sad,0.055; disgusted,0.055 expectant,0.2; praiseful,0.175;happy,0.11 8 happy,0.429; paiseful,0.039 critical,0.039 love,0.75; sad,0.413; praiseful,0.338 expectant,0.456; praiseful,0.413; sad,0.206 happy,0.533; love,0.2; critical,0.175 love,0.279; angry,0.039

happy

26

expectant

27

expectant

28

expectant 29 critical

30

love

31

love

32

love

33

love

34

expectant 35 happy

36

love

37 critical,0.023

expectant

38 annoyed,0.2

happy

39 angry,0.2

sad,0.338; critical,0.175; depressed,0.175 praiseful,0.556; expectant,0.2; happy,0.069 sad,0.55; praiseful,0.037; happy,0.2 expectant,0.338

expectant

22

24

7.2.

angry,0.372; praiseful,0.269; love,0.234 love,0.275; Sad,0.075; praiseful,0.025 thankful,0.2; happy,0.092; sad,0.092 love,0.469; happy,0.338; sad,0.2 happy,0.338; praiseful,0.2; annoyed,0.092 love,0.338; expectant,0.2; sad,0.175 praiseful,0.425; love,0.05

love

40

love,0.2 love,0.2 thankful,0.2 love,0.2 happy,0.2 expectant,0.2 praiseful,0.35 sad,0.2 praiseful,0.35 sad,0.35 annoyed,0.2 expectant,0.2 happy,0.35 praiseful,0.2 praiseful,0.2 happy,0.35 critical,0.35 love,0.35 praiseful,0.2 love,0.069 love,0.175 expectant,0.2 happy,0.35 love,0.35 expectant,0.3 5 happy,0.35 love,0.2

Neutral,0 critical,0.35

happy,0.35 surprised,0.35

Table 7. The percentage of the open test

1

Number of Incorrect Prediction 7

Number of Correct Prediction 33

Percentage of Correct Prediction (%) 82.5

2

12

28

70

Method

expectant,0.2

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7.3.

Comparison and discussion

In comparing the two methods for extracting emotion from a blog article, we can see that Method 1 achieves a better performance than Method 2. However, Method 1 spends much more time than Method 2 doing the emotion computing. As shown in Figure 2, the solid line denoting emotion prediction of Method 1 demonstrates more richness and accuracy in emotion description than the dot-dash line denoting that of Method 2. The data used in Figure 2 come from the first 10 articles of one blog author in Table 4. The visual image of the emotion changes for 10 days can be easily obtained from Figure 2. If teachers or psychological consultants have the emotion graph everyday, they can monitor the mental situations of college students and intervene to prevent the depression in time.

Figure 2. Comparison of two methods of emotion computing By analyzing the incorrect predictions of emotion, we can see that for both of the two methods, there exit some articles in which the emotion of the central sentences can not be sensed for the lack of vocabularies in the emotion dictionary or the lack of the syntax information. For Method 1, the cases of incorrect prediction are: When the weight value of some emotion category (different from the emotion of the central sentence) becomes the biggest after added throughout the whole article, this emotion category will be the emotion of the article. However, since Method 2 detects the emotion according to the importance of paragraphs, in some cases it gives the correct answer directly and quickly. When the weight values of several emotion categories appear to be the same, Method 1 can not predict the emotion of the article correctly. For Method 2, the cases of incorrect prediction are: Because the searching order in Method 2 is fixed, the emotion first met in the article is probably not the emotion of the central sentence. When the manual evaluation of emotion is

“neutral”, Method 2 can only examine one of all the emotions throughout the whole article as the emotion of the article, while Method 1 can examine all the emotions throughout the whole article instead of a simple “neutral”. In the experiments for testing, we choose 40 articles of two blog authors for close test and 40 articles of other blog authors for open test. The result has demonstrated that the methods used in our system are reasonable, and the integration of the two methods may achieve a better performance. 8.

Conclusions and future work

Recently, the number of college students who suicide themselves has been increasing with amazing speed in the world. The problem of mental health among college students has raised greater attention from a lot of people in many professions. With the popularization of internet and the development of information processing technologies, more and more people have established their own blog websites. It will be convenient for teachers or psychological consultants to monitor the affective information expressed in blog articles in order to prevent the depression of students. The advances in Affective Computing and Natural Language Processing enable the emotion recognition from blog articles automatically. In this paper, we have outlined the approach to develop a Blog Emotion-Recognizing System. Firstly we decided the classification of emotions, and introduced the model of the System. Based on the lexical contents of words and structural characteristics of blog articles, two methods have been proposed for emotion computing. The experiments for testing have been carried out, and the results for the two methods were analyzed and discussed. The approach was proved to be feasible and pointed out the future’s direction of the research. From the comparison and discussion of experimental results, we find that some parts need to be improved in the future: The first task to improve the emotion dictionary by expanding the vocabularies and arranging them better, for it plays a very important role in the emotion sensing. Since different people have different styles in writing blog articles, in the future we will choose more blog articles of different authors for the algorithm analysis and system test, which are supposed to achieve a better performance. Acknowledgements We would like to thank Motoyuki Suzuki, Seiji Tsuchiya, Kazuyuki Matsumoto, Li Yun, Teng Zhi, Yuan Caixia and Mohammad Golam Sohrab for their

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valuable advices while doing the experiments. This research has been partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (B), 19300029. References [1] James, R. K., & Gilliland B. E., “Crisis Intervention Strategies (5th Edition)”, Brooks/Cole, 2005. [2] R. W. Picard, “Affective Computing”, The MIT Press, Mass., 1997. [3] P. W. Picard, E. Vyzas, J. Healey, “Toward Machine Emotional Intelligence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No.10, pp. 1175-1191, October 2001. [4] P. Pani, N. Sarkar, J. Adams, “Anxiety-based affective communication for implicit human-machine interaction”, Advanced Engineering Informatics 21 (3), pp. 323-334, July 2007. [5] D. Kulic, E. A Croft, “Affective state estimation for human-robot interaction”, IEEE Transactions on Robotics 23 (5), pp. 991-1000, Oct. 2007. [6] N. Fragopanagos, J.G. Taylor, “Emotion recognition in human–computer interaction”, Neural Networks, vol. 18, pp. 389–405, 2005. [7] P. Jiang, H. Xiang, F. J. Ren, S. Kuroiwa, “An Advanced Mental State Transtion Network and Psychological Experiments”, Lecture Notes in Computer Science, Vol. 3824, pp.1026-1035, Springer-Verlag GmbH, 2005. [8] H. Liu, H. Lieberman, T. Selker, ”A Model of Textual Affect Sensing using Real-world Knowledge”, Proceedings of the 2003 International Conference on Intelligent User

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