在本研究中，我們利用微網誌所提供的四個資訊：驚嘆號、表情符號、重複情緒字、重複字母來分析每個情緒字所隱含的情緒強度。當使用者在微網誌中使用情緒字來表達個人對於某個事物的觀感時，經常會使用這四個資訊來傳達其所隱含的情緒強度。在本研究中，我們首先將每則微網誌訊息中的情緒字及四個相對應的情緒強度資訊擷取出來，形成字與強度資訊的陣列。透過基因演算法的學習方式，針對每個情緒字找出一個最佳的參數，透過參數的設定來計算出每個情緒字在微網誌中所隱含的強度。在實驗中，我們蒐集使用者的主觀意見，比較實驗結果與使用者的主觀意見使否符合。情緒強度可以用來提供使用者在進行決策時一個重要的參考依據，同時也提供情緒研究上另一個層面的思考。實驗結果顯示，透過微網誌的四個資訊，可以判斷出每個字所隱含的情緒強度，這些強度和使用者的主觀認定相符合。

MicroBlogs have become a popular and important communication platform for Internet users. Many people frequently use the platform to share opinions about different topics. Although a few research papers have employed the use of MicroBlogs for sentiment analysis, the focus has been limited to classifying user sentiment into positive and negative classes. In this paper, we introduce an approach to identify the sentiment strength of words in MicroBlogs. Given a sentiment word, we extract the corresponding strength attributes from MicroBlogs: repeated exclamations, emoticons, repeated words, and repeated characters. By integrating these attributes with genetic algorithms, comparable word strength is provided. The experimental results demonstrate that the proposed approach satisfies the expectations of users and effectively provides comparable metrics on the sentiment strength of words.

Chinese Abstract ii
Abstract iii
Acknowledgement iv
List of Tables x
List of Figures xi
1 INTRODUCTION 1
2 RELATEDWORK 4
3 METHODOLOGY 7
3.1 Sentiment Words Collection . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1.1 Sentiment Seeds . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.1.2 Words Checker . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Strength Attributes Extraction . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2.1 MicroBlog Repository . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2.2 Strength Attributes Extractor . . . . . . . . . . . . . . . . . . . . . 11
3.3 Sentiment Strength Integration . . . . . . . . . . . . . . . . . . . . . . . . 14
4 EXPERIMENT 18
4.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.2 Users’ Satisfaction Degree . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3 Comparable Sentiment Words Diagram . . . . . . . . . . . . . . . . . . . 21
5 CONCLUSION 24
References 25

[1] http://apiwiki.twitter.com/.
[2] J. Bollen, A. Pepe, and H. Mao. Modeling public mood and emotion: Twitter senti-
ment and socio-economic phenomena. CoRR, abs/0911.1583, 2009.
[3] K. Ehrlich and N. S. Shami. Microblogging inside and outside the workplace. In Pro-
ceeding of the Fourth International AAAI Conference on Weblogs and Social Media,
2010.
[4] V. Hatzivassiloglou and K. McKeown. Predicting the semantic orientation of adjec-
tives. In Proceedings of the 35th Annual Meeting of the Association for Computa-
tional Linguistics and Eighth Conference of the European Chapter of the Association
for Computational Linguistics, page 181. Association for Computational Linguistics,
1997.
[5] V. Hatzivassiloglou and J. Wiebe. Effects of adjective orientation and gradability
on sentence subjectivity. In Proceedings of the 18th conference on Computational
linguistics-Volume 1, page 305. Association for Computational Linguistics, 2000.
[6] J. H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis
with Applications to Biology, Control, and Artificial Intelligence. The MIT Press,
April 1992.
[7] A. Huang, D. Yen, and X. Zhang. Exploring the potential effects of emoticons. Infor-
mation & Management, 45(7):466–473, 2008.
[8] C. A. Jenny Preece, Diane Maloney-Krichmar. History of online communities. In
Encyclopedia of Community: From Village to Virtual World., 2003.
[9] D. Lin. Automatic retrieval and clustering of similar words. In Proceedings of the
17th international conference on Computational linguistics-Volume 2, pages 768–
774. Association for Computational Linguistics, 1998.
[10] Y. Lu, X. Kong, X. Quan, W. Liu, and Y. Xu. Exploring the Sentiment Strength of
User Reviews. Web-Age Information Management, pages 471–482, 2010.
[11] G. A.Miller, R. Beckwith, C. Fellbaum, D. Gross, and K.Miller. Wordnet: An on-line
lexical database. International Journal of Lexicography, 3:235–244, 1990.
[12] B. Pang and L. Lee. A sentimental education: Sentiment analysis using subjectivity
summarization based on minimum cuts. In Proceedings of the ACL, 2004.
[13] J. Read. Using emoticons to reduce dependency in machine learning techniques for
sentiment classification. In Proceedings of the ACL Student Research Workshop,
pages 43–48. Association for Computational Linguistics, 2005.
[14] E. Riloff and J. Wiebe. Learning extraction patterns for subjective expressions.
In Proceedings of the 2003 conference on Empirical methods in natural language
processing-Volume 10, page 112. Association for Computational Linguistics, 2003.
[15] G. Strauss and D. Allen. Emotional intensity and categorisation ratings for emotional
and nonemotional words. Cognition & Emotion, 22(1):114–133, 2008.
[16] G. Strauss and D. Allen. Emotional intensity and categorisation ratings for emotional
and nonemotional words. Cognition & Emotion, 22(1):114–133, 2008.
[17] M. Yuasa, K. Saito, and N. Mukawa. Emoticons convey emotions without cognition
of faces: an fmri study. In CHI ’06: CHI ’06 extended abstracts on Human factors in
computing systems, pages 1565–1570, New York, NY, USA, 2006. ACM.