Product Aspect Extraction Supervised With Domain Knowledge

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. 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. Existing methods either generate multiple fine-grained aspects without proper categorization or categorize semantically unrelated product aspects(e.g., by unsupervised topic modeling). In this thesis, we first examine previous studies on product aspect extraction. To overcome the limitations of existing methods, two novel semi-supervised models for product aspect extraction are then proposed. More specifically, the proposed methodology first extracts seeding aspects and related terms from detailed product descriptions readily available on E-commerce websites. Next, product reviews are regrouped according to these seeding aspects so that more effective textual contexts for topic modeling are built. Finally, two novel semi-supervised topic models are developed to extract human-comprehensible product aspects. For the first proposed topic model, the Fine-grained Labeled LDA(FL-LDA), seeding aspects are applied to guide the model to discover words that are related to these seeding aspects. For the second model, the Unified Fine-grained Labeled LDA(UFL-LDA), we incorporate unlabeled documents to extend the FL-LDA model so that words related to the seeding aspects or other high-frequency words in customer reviews are extracted. Our experimental results demonstrate that the proposed methods outperform state-of-the-art methods on aspect extraction.