Classifying mood in plurks

Classifying mood in plurks Mei-Yu Chen* meifish.kat@gmail.com Hsin-Ni Lin* hnlin98@std.ntnu.edu.tw Chang-An Shih** cashih0805@gmail.com Yen-Ching Hsu* depend2030@gmail.com Pei-Yu Hsu* moon80206@gmail.com Shu-Kai Hsieh* shukai@gmail.com *Department of English, National Taiwan Normal University ** Institute of Linguistics, Academia Sinica Abstract In this paper, we present a simple but efficient approach for the automatic mood classification of microblogging messages from Plurk platform. In contrast with Twitter, Plurk has become the most popular microblogging service in Taiwan and other countries1; however, no previous research has been done for the emotion and mood recognition, nor the Chinese affective terms or corpus available. Following the line of mashup programming, we thus construct a dynamic plurk corpus by pipelining Plurk APIs, Yahoo! Chinese segmentation APIs, etc to preprocess and annotate the corpus data. Based on the corpus, we conduct experiments by way of combining textual statistics and emoticons data, and our method yield the results with high performance. This work can be further extended to combine with affective ontology designed with emotion theory of appraisal. Keyword: mood classification, plurks, keyness, emotion paradox 1 According to Alvin, the cofounder of Plurk website, the number of the plurkers in Taiwan had reached approximately 1 million, which was one-third of the total plurkers in October, 2009. Another statistic data is collected from Google trend for website, manifesting that Taiwan is the rank one region of visiting Plurk website (August,2010). 172 Proceedings of the 22nd Conference on Computational Linguistics and Speech Processing (ROCLING 2010), Pages 172-183, Puli, Nantou,Taiwan, September 2010. 1. Introduction Recently, with the emergence of Web 2.0 content, sentiment and opinion mining in social media and network domains like Blogs, has gained great attention from computational linguistics and other fields. Mood/Emotion classification, as related to sentiment classification, whose goal is set to tag a text based on whether it expresses positive or negative sentiment, has been surveyed on lyrics in music domain. Mood classification is useful for various applications; the mood intelligence module can be incorporated in (affective) dialogue systems, learning tutors, etc. to improve the naturalness of human-machine interaction. Figure 1. Number of Plurkers This paper addresses the task of classifying microblogging posts by mood. What motivates us to conduct this research is the fact that the above figure shows: Plurk2 is the most commonly used microblogging platform in Asia, and in contrast with other social networks, it has also become one of the most common forms of computer-mediated communication in Taiwan. In this paper, we aim to propose a simple and effective method in automatic classifying emotion of plurk messages for emotion. The rest of the paper is organized as follows. Section 2 introduces the emoticons in plurk messages. Section 3 describes the construction of plurk corpus. Section 4 presents the experiment results and evaluation. Finally, Section 5 concludes the paper and summarizes our future work. 2. Emoticon and Emotional Expression in Social Media In the previous studies, computer systems have been designed to perform automatic sentiment analysis using elements from machine learning such as latent semantic analysis (LSA), …

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