Toward a fuzzy domain sentiment ontology tree for sentiment analysis

Abstract

Sentiment analysis is a kind of text classification that classifies texts based on the sentiment orientation of opinions they contain. Sentiment analysis of product reviews has recently become very popular in Web text mining, natural language processing and computational linguistics research. Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations' business strategy development and individual consumers' comparison shopping. The main contribution of this paper is the illustration of a novel feature-level sentiment analysis mechanism which is underpinned by a fuzzy domain sentiment ontology tree extraction algorithm. The proposed mechanism can automatically construct fuzzy domain ontology tree (FDSOT) based on the product reviews, including the extraction of sentiment words, product features and the relations among features. Here product features (or features) mean product components and attributes. Evaluated based on Chinese product reviews collected from 360buy.com, the experiments show that our research approach improves the accuracy of polarity predictions.