SSAM: toward Supervised Sentiment and Aspect Modeling on different levels of labeling

摘要

In recent years, people want to express their opinion on every online service or product, and there are now a huge number of opinions on the social media, online stores and blogs. However, most of the opinions are presented in plain text and thus require a powerful method to analyze this volume of unlabeled reviews to obtain information about relevant details in minimum time and with a high accuracy. In this paper, we propose a supervised model to analyze large unlabeled opinion data sets. This model has two phases: preprocessing and a Supervised Sentiment and Aspect Model (SSAM) which is an extended version of Latent Dirichlet Allocation Model. In the preprocessing phase, we input thousands of unlabeled opinions and received a set of (key, value) pairs in which a key holds a word or an opinion and a value holds supervised information such as a sentiment label of this word or opinion. After that we give these pairs to the proposed SSAM algorithm, which incorporates different levels of supervised information such as (document and sentence) levels or (document and term) levels of supervised information, to extract and cluster aspects related to a sentiment label and also classify opinions based on their sentiments. We applied SSAM to reviews of electronic devices and books from Amazon. The experiments show that the aspects found by SSAM capture more important aspects that are closely coupled with a sentiment label, and also in sentiment classification SSAM outperforms other topic models and comes close to supervised methods.