Finally, two novel semi-supervised topic models are developed to extract human-comprehensible product aspects. Next, product reviews are regrouped according to these seeding aspects so that more effective textual contexts for topic modeling are built. More specifically, the proposed methodology first extracts seeding aspects and related terms from detailed product descriptions readily available on E-commerce websites. To overcome the limitations of existing methods, two novel semi-supervised models for product aspect extraction are then proposed. In this paper, we first examine previous studies on product aspect extraction.
Existing methods either generate multiple fine-grained aspects without proper categorization or categorize semantically unrelated product aspects (e.g., by unsupervised topic modeling). Although a number of methods of aspect extraction have been proposed before, very few of them are designed to improve the interpretability of generated aspects. One of the most challenging problems in aspect-based opinion mining is aspect extraction, which aims to identify expressions that describe aspects of products (called aspect expressions) and categorize domain-specific synonymous expressions.
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