Abstract-We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multiaspect sentence labeling and multi-aspect rating prediction. For sentence labeling, we propose a weakly-supervised approach that utilizes only minimal prior knowledge-in the form of seed words-to enforce a direct correspondence between topics and aspects. This correspondence is used to label sentences with performance that approaches a fully supervised baseline. For multi-aspect rating prediction, we find that overall ratings can be used in conjunction with our sentence labelings to achieve reasonable performance compared to a fully supervised baseline. When gold-standard aspect-ratings are available, we find that topic model based features can be used to improve unsophisticated supervised baseline performance, in agreement with previous multi-aspect rating prediction work. This improvement is diminished, however, when topic model features are paired with a more competitive supervised baseline-a finding not acknowledged in previous work.
REF perform multi-aspect sentiment analysis using a topic model.
Multi-aspect Sentiment Analysis with Topic Models
2011 IEEE 11th International Conference on Data Mining Workshops
2011 IEEE 11th International Conference on Data Mining Workshops
Computer Science