An unsupervised aspect detection model for sentiment analysis of reviews

Bagheri, A; Saraee, MH; Jong, F

Abstract

With the rapid growth of user-generated content on the internet, sentiment analysis of online reviews has become a hot research topic recently, but due to variety and wide range of products and services, the supervised and domain-specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for aspects. In this paper, we propose an unsupervised model for detecting aspects in reviews. In this model, first a generalized method is proposed to learn multi-word aspects. Second, a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Third a new metric based on mutual information and aspect frequency is proposed to score aspects with a new bootstrapping iterative algorithm. The presented bootstrapping algorithm works with an unsupervised seed set. Finally two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. The proposed model does not require labeled training data and can be applicable to other languages or domains. We demonstrate the effectiveness of our model on a collection of product reviews dataset, where it outperforms other techniques.

Citation

Bagheri, A., Saraee, M., & Jong, F. (2013). An unsupervised aspect detection model for sentiment analysis of reviews. Lecture notes in computer science, 7934, 140-151. https://doi.org/10.1007/978-3-642-38824-8_12

• Journal Article Type: Article
• Publication Date: Jan 1, 2013
• Deposit Date: Sep 2, 2013
• Journal: 2013 Lecture Notes in Computer Science
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 7934
• Pages: 140-151
• Book Title: Natural Language Processing and Information Systems
• DOI: https://doi.org/10.1007/978-3-642-38824-8_12
• Publisher URL: http://dx.doi.org/10.1007/978-3-642-38824-8_12
• Related Public URLs: http://link.springer.com/chapter/10.1007%2F978-3-642-38824-8_12; http://link.springer.com/bookseries/558