Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Papers -- Data Mining |
An Unsupervised Opinion Mining Approach for Japanese Weblog Reputation Information Using an Improved SO-PMI Algorithm
1 The authors are with the Graduate School of Information Science and Technology, Hokkaido University, Sapporo-shi, 060–0814 Japan. E-mail: wgw{at}media.eng.hokudai.ac.jp
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Abstract |
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In this paper, we propose an improved SO-PMI (Semantic Orientation Using Pointwise Mutual Information) algorithm, for use in Japanese Weblog Opinion Mining. SO-PMI is an unsupervised approach proposed by Turney that has been shown to work well for English. When this algorithm was translated into Japanese naively, most phrases, whether positive or negative in meaning, received a negative SO. For dealing with this slanting phenomenon, we propose three improvements: to expand the reference words to sets of words, to introduce a balancing factor and to detect neutral expressions. In our experiments, the proposed improvements obtained a well-balanced result: both positive and negative accuracy exceeded 62%, when evaluated on 1,200 opinion sentences sampled from three different domains (reviews of Electronic Products, Cars and Travels from Kakaku.com). In a comparative experiment on the same corpus, a supervised approach (SA-Demo) achieved a very similar accuracy to our method. This shows that our proposed approach effectively adapted SO-PMI for Japanese, and it also shows the generality of SO-PMI.
Key Words: SO-PMI, opinion mining, Weblog reputation information, sentiment analysis, unsupervised learning, supervised learning
Manuscript received May 24, 2007. Manuscript revised November 30, 2007.