Word of mouth quality classification based on contextual sentiment lexicons

Word of mouth (WOM), also known as the passing of information from person to person or opinionated text, has become the main information resource for consumers when making purchase decisions. Whether WOM is a valuable reference source for consumers making a purchase is determined by the quality of the WOM. WOM quality classification is useful in filtering significant WOM documents from insignificant ones, and helps consumers to make their purchase decisions more efficiently. When a consumer has a negative experience, a lower rating score and negative text are generally provided and vice versa. Regardless of the sentimental polarity, high-quality WOM (i.e. with a very high or very low rating score) has a stronger influence on consumer behavior than low-quality WOM (i.e. with a medium rating score). We build three contextual lexicons to maintain the relationship between words and their associated sentimental categories. We then apply the technique of preference vector modeling and evaluate our proposed approach by four classifiers. According to the experiments for the internet movie database (IMDb) polarity data set and hotels.com data set, the proposed contextual lexicon-concept-quality (CLCQ) and contextual lexicon-quality (CLQ) models outperform the benchmarks, i.e. the static first-sense SentiWordNet and average-sense SentiWordNet models. These results demonstrate that the proposed models can be used as a viable approach for WOM quality classification. The novel aspects of this paper are three-fold. Firstly, we focus on WOM quality classification instead of traditional sentimental polarity classification. Secondly, we build sentiment lexicons from the contextual information, which are adaptable to domains. Thirdly, we integrate these contextual sentiment lexicons with preference vector modeling for WOM quality classification and achieve an outstanding improvement.