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In recent years, the number of freely available online reviews is increasing at a high speed. Aspect-based opinion mining technique has been employed to find out reviewers' opinions toward different product aspects. Such finer-grained opinion mining is valuable for the potential customers to make their purchase decisions. Product-feature extraction and categorization is very important for better mining aspect-oriented opinions. Since people usually use different words to describe the same aspect in the reviews, product-feature extraction and categorization becomes more challenging. Manually product-feature extraction and categorization is tedious and time consuming, and practically infeasible for the massive amount of products. In this paper, we propose an unsupervised product-feature categorization method with multilevel latent semantic association. After extracting product-features from the semi-structured reviews, we construct the first latent semantic association (LaSA) model to group words into a set of concepts according to their virtual context documents. It generates the latent semantic structure for each product-feature. The second LaSA model is constructed to categorize the product-features according to their latent semantic structures and context snippets in the reviews. Experimental results demonstrate that our method achieves better performance compared with the existing approaches. Moreover, the proposed method is language- and domain-independent.
Guo et al. (Mon,) studied this question.