Extracting Domain-Specific Opinion Words for Sentiment Analysis

Abstract

In this paper, we consider opinion word extraction, one of the key problems in sentiment analysis. Sentiment analysis (or opinion mining) is an important research area within computational linguistics. Opinion words, which form an opinion lexicon, describe the attitude of the author towards certain opinion targets, i.e., entities and their attributes on which opinions have been expressed. Hence, the availability of a representative opinion lexicon can facilitate the extraction of opinions from texts. For this reason, opinion word mining is one of the key issues in sentiment analysis. We designed and implemented several methods for extracting opinion words. We evaluated these approaches by testing how well the resulting opinion lexicons help improve the accuracy of methods for determining the polarity of the reviews if the extracted opinion words are used as features. We used several machine learning methods: SVM, Logistic Regression, Naive Bayes, and KNN. By using the extracted opinion words as features we were able to improve over the baselines in some cases. Our experiments showed that, although opinion words are useful for polarity detection, they are not sufficient on their own and should be used only in combination with other features.