Paper: A Sentiment-aligned Topic Model for Product Aspect Rating Prediction

ACL ID D14-1126
Title A Sentiment-aligned Topic Model for Product Aspect Rating Prediction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Aspect-based opinion mining has attracted lots of attention today. In this paper, we address the problem of product aspect rat- ing prediction, where we would like to ex- tract the product aspects, and predict as- pect ratings simultaneously. Topic mod- els have been widely adapted to jointly model aspects and sentiments, but exist- ing models may not do the prediction task well due to their weakness in sentiment extraction. The sentiment topics usually do not have clear correspondence to com- monly used ratings, and the model may fail to extract certain kinds of sentiments due to skewed data. To tackle this prob- lem, we propose a sentiment-aligned topic model(SATM), where we incorporate two types of external knowledge: product- level overall rating distribution and word- level sentiment ...