For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
An Unsupervised Opinion Mining Approach for Japanese Weblog Reputation Information Using an Improved SO-PMI Algorithm
Guangwei WANG Kenji ARAKI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2008/04/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Data Mining
SO-PMI, opinion mining, Weblog reputation information, sentiment analysis, unsupervised learning, supervised learning,
Full Text: PDF>>
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.