Publication

Abstract : Understanding the tastes of each user and the characteristics of each product is necessary to predict how a user will respond to a new product. These latent user and product dimensions can be discovered with the help of user feedback. A numeric rating and its accompanying text review is the most widely available form of user feedback. A measure which encapsulates the contents of such reviews is often necessary as they have been found to significantly influence the shopping behavior of users. A fine-grained form of such measure that could act as a perfect feedback about the product is star rating. The review rating prediction tries to predict a rating corresponding to the given review. An approach that performs review rating prediction task by using the latent topics extracted from reviews and their associated sentiments is proposed in this paper. The proposed approach treats review rating prediction problem as a multi-class classification problem. An empirical review of topic modeling techniques such as (i) term frequency-inverse document frequency(TF-IDF), (ii) latent Dirichlet allocation(LDA), and (iii) nonnegative matrix factorization(NNMF) is performed in this work to investigate their efficiency. The proposed approach has a lot of advantages: Firstly, this approach accurately predicts product ratings by making use of the topics and their sentiments present in the reviews; this is useful in occasions where only reviews are available. Secondly, the discovered topics and their sentiments can be used to recognize informative reviews. Thirdly, it facilitates to justify the rating with review text. The experimental results show that the proposed model works well with latent topics and sentiments when the underlying model is trained using a deep learning technique.