Abstract
Sentiment Analysis has been a hot research topic in recent years. Emotion classification is more detailed sentiment analysis which cares about more than the polarity of sentiment. In this paper, we present our system of emotion analysis for the Sina Weibo texts on both the document and sentence level, which detects whether a text is sentimental and further decides which emotion classes it conveys. The emotions of focus are seven basic emotion classes: anger, disgust, fear, happiness, like, sadness and surprise. Our baseline system uses supervised machine learning classifier (support vector machine, SVM) based on bag-of-words (BoW) features. In a contrast system, we propose a novel approach to construct an emotion lexicon and to generate a new feature representation of text which is named emotion vector eVector. Our experimental results show that both systems can classify emotion significantly better than random guess. Fusion of both systems obtains additional gain which indicates that they capture certain complementary information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, vol. 271. Association for Computational Linguistics (2004)
Li, N., Wu, D.D.: Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems 48(2), 354–368 (2010)
Tumasjan, A., Sprenger, T.O., Sandner, P.G., et al.: Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. In: ICWSM, vol. 10, pp. 178–185 (2010)
O’Connor, B., Balasubramanyan, R., Routledge, B.R., et al.: From tweets to polls: Linking text sentiment to public opinion time series. ICWSM 11, 122–129 (2010)
Kamps, J., Marx, M.J., Mokken, R.J., et al.: Using wordnet to measure semantic orientations of adjectives (2004)
Zhu, Y.L., Min, J., Zhou, Y., et al.: Semantic orientation computing based on HowNet. Journal of Chinese Information Processing 20(1), 14–20 (2006); 朱嫣岚,闵锦,周雅倩,等.: 基于HowNet的词汇语义倾向计算.中文信息学报 20(1), 14–20 (2006)
Dun, L., Fuyuan, C., Yuanda, C., et al.: Text Sentiment Classification Based on Phrase Patterns. Computer Science 35(4), 132-134 (2008); 李钝,曹付元,曹元大,等.基于短语模式的文本情感分类研究.计算机科学 35(4), 132–134 (2008)
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics (2002)
Lewis, D.D.: Naive (Bayes) at forty: The independence assumption in information retrieval. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 4–15. Springer, Heidelberg (1998)
Konig, R., Renner, R., Schaffner, C.: The operational meaning of min-and max-entropy. IEEE Transactions on Information Theory 55(9), 4337–4347 (2009)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Benamara, F., Cesarano, C., Picariello, A., et al.: Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone. In: ICWSM (2007)
Lin, R.G., Tsai, T.C.: Scalable System for Textual Analysis of Stock Market Prediction. In: The Third International Conference on Data Analytics 2014, pp. 95–99 (2014)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3), 27 (2011)
Wu, Y., Kita, K., Ren, F., Matsumoto, K., Kang, X.: Exploring Emotional Words for Chinese Document Chief Emotion Analysis. In: Proceedings of PACLIC 2011, pp. 597–606 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, C., Wu, H., Jin, Q. (2014). Emotion Classification of Chinese Microblog Text via Fusion of BoW and eVector Feature Representations. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-662-45924-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45923-2
Online ISBN: 978-3-662-45924-9
eBook Packages: Computer ScienceComputer Science (R0)