Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 4 - Volume 04
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
IEICE - Transactions on Information and Systems
An empirical approach for opinion detection using significant sentences
AMT'10 Proceedings of the 6th international conference on Active media technology
Pattern and keyword based opinion analysis from opinionated texts
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Detection of web users' opinion from multimodal opinion elements
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Detection of web users' opinion from normal and short opinionated words
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Mining co-occurrence matrices for SO-PMI paradigm word candidates
EACL '12 Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics
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We propose a variation of the SO-PMI algorithm for Japanese, for use in Weblog Opinion Mining. SO-PMI is an unsupervised approach proposed by Turney that has been shown to work well for English. We first used the SO-PMI algorithm on Japanese in a way very similar to Turney's original idea. The result of this trial leaned heavily toward positive opinions. We then expanded the reference words to be sets of words, tried to introduce a balancing factor and to detect neutral expressions. After these modifications, we achieved a well-balanced result: both positive and negative accuracy exceeded 70%. This shows that our proposed approach not only adapted the SO-PMI for Japanese, but also modified it to analyze Japanese opinions more effectively.