We investigate the efficacy of topic model based approaches to two multi-aspect sentiment analysis tasks: multi-aspect 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. Copyright © 2011 IEEE.
|Title of host publication||Multi-aspect sentiment analysis with topic models. 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW)|
|Editors||Myra SPILIOPOULOU, Haixun WANG, Diane COOK, Jian PEI, Wei WANG, Osmar ZAÏANE, Xindong WU|
|Place of Publication||Los Alamitos|
|Publication status||Published - 2011|
CitationLu, B., Ott, M., Cardie, C., & Tsou, B. K. (2011). Multi-aspect sentiment analysis with topic models. In M. Spiliopoulou, et al. (Eds.), 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW) (pp. 81-88). Los Alamitos: IEEE.
- Multi-aspect sentiment analysis
- Topic modeling