Opinion Extraction based on Syntactic Pieces

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

The Today Tendency of Sentiment Classification

Sentiment classification has already been studied for many years because it has had many crucial contributions to many different fields in everyday life, such as in political activities, commodity production, and commercial activities. There have been many kinds of the sentiment analysis such as machine learning approaches, lexicon-based approaches, etc., for many years. The today tendency of the sentiment classification is as follows: (1) Processing many big data sets with shortening execution times (2) Having a high accuracy (3) Integrating flexibly and easily into many small machines or many different approaches. We will present each category in more details

Modifying SO-PMI for Japanese Weblog Opinion Mining by using a balancing factor and detecting neutral expressions

We propose a variation of the SO-PMI al-gorithm for Japanese, for use in Weblog Opinion Mining. SO-PMI is an unsuper-vised 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 fac-tor 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 modi-fied it to analyze Japanese opinions more effectively.