Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews

The dramatic increase in the use of smartphones has allowed people to comment on various products at any time. The analysis of the sentiment of users’ product reviews largely depends on the quality of sentiment lexicons. Thus, the generation of high-quality sentiment lexicons is a critical topic. In this paper, we propose an automatic approach for constructing a domain-specific sentiment lexicon by considering the relationship between sentiment words and product features in mobile shopping reviews. The approach first selects sentiment words and product features from original reviews and mines the relationship between them using an improved pointwise mutual information algorithm. Second, sentiment words that are related to mobile shopping are clustered into categories to form sentiment dimensions. At each sentiment dimension, each sentiment word can take the value of 0 or 1, where 1 indicates that the word belongs to a particular category whereas 0 indicates that it does not belong to that category. The generated lexicon is evaluated by constructing a sentiment classification task using several product reviews written in both Chinese and English. Two popular non-domain-specific sentiment lexicons as well as state-of-the-art machine-learning and deep-learning models are chosen as benchmarks, and the experimental results show that our sentiment lexicons outperform the benchmarks with statistically significant differences, thus proving the effectiveness of the proposed approach.