Automatic Sentiment Analysis for Web User Reviews

The web contains a wealth of user reviews about a topic concerning positive, negative or mixed mood, but it is a daunting task to discriminate them manually. This paper presents an approach for automatic sentiment analysis by 1) generating positive and negative sentiment words from Tongyici Cilin with paradigm words, 2) determining the sentiment orientation of ambiguous words according to their contexts, 3) setting up proper weight factors to different part-of-speeches, and 4) expanding the initial sentiment words by an iterative process. The performance of our approach is verified on web user reviews and achieves an average precision of 83.52%.