Opinion mining of hotel customer-generated contents in Chinese weblogs

An opinion mining method based on feature-based sentiment classification is proposed to extract the online electronic word-of-mouth on weblogs in Taiwan and can demonstrate its effectiveness with acceptable classification and forecasting performances.

Abstract

Customer-generated contents in weblogs provide tourism organisations with valuable market intelligence and ongoing market research opportunities. In this study, an opinion mining method based on feature-based sentiment classification is proposed to extract the online electronic word-of-mouth on weblogs in Taiwan. For opinion extraction, a supervised semantic orientation using the point-wise mutual information (SO_PMI) algorithm based on the extension of Turney's unsupervised SO_PMI algorithm is proposed to extract the opinion words. In addition, a heuristic n-phrase rule is proposed to find out customer opinions about hotel attributes, including hotel image, services, price/value, food and beverage, room, amenities, and location. The experimental results show that the proposed approach mixed with supervised SO_PMI algorithm and heuristic n-phrase rule can demonstrate its effectiveness with acceptable classification and forecasting performances. Furthermore, a perceptual map based on correspondence analysis visually presents opinions comparison to provide the insight of competitive positions.