Chang-An Shih

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 countries 1; 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 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). Proceedings of the 22nd Conference on Computational Linguistics and Speech Processing (ROCLING 2010), Pages 172-183, Puli, Nantou,Taiwan, September 2010.