SLTM: A Sentence Level Topic Model for Analysis of Online Reviews

Due to large amounts of reviews for many similar online products, users often feel difficult to determine which products have the most desirable features that they want. In this paper, we propose a model-based approach to analyzing online reviews and identifying the strengths and weaknesses of a product by its product features. We propose a Sentence Level Topic Model (SLTM), which can classify review sentences into different classes corresponding to different product features. The model contains a hidden layer, called the topic layer, between corpus and words. Once a SLTM has been trained with sufficient labeled data points, it can identify the most related topic (i.e., product feature) for each sentence. To capture a reviewer’ opinion, we perform sentiment analysis for each review sentence, and derive the weighted feature preference vectors for the review. Finally, we combine the results of all review comments for a product into a review summary. The case study shows that by comparing the review summaries of similar online products, users may have a much easier time to find their desired products. Keywords-Electronic commerce; product review; product feature; topic model; feature extraction; sentiment analysis.