Abstract
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.
Highlights
With the rapid development of smartphones, mobile shopping, which is already popular, is expected to grow faster
These results indicate that the domainspecific lexicon, which is constructed from the corresponding corpus, shows better performance for sentiment classification tasks on shopping reviews
A sentiment matrix that considers the relationship between sentiment words and product features is built
Summary
With the rapid development of smartphones, mobile shopping, which is already popular, is expected to grow faster. Sentiment classification can be performed using machine-learning, lexicon-based, and hybrid approaches. Ortigosa et al [4] developed a lexicon from a corpus and chose sentiment words along with the labeled class as the input features for a machine-learning classification method. We present a novel method to construct a domain-specific sentiment lexicon by mining the relationship between sentiment words and product features in a specific corpus. First, a sentiment matrix is constructed based on the relationship between sentiment words and product features. The experimental results show that the filtering of product features and the application of the EPMI algorithm can greatly improve the performance of our lexicon for mobile shopping reviews.
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