Multi-aspect sentiment analysis with topic models

Bin LU, Myle OTT, Claire CARDIE, Ka Yin Benjamin TSOU

Research output: Chapter in Book/Report/Conference proceedingChapters

197 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationMulti-aspect sentiment analysis with topic models. 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW)
EditorsMyra SPILIOPOULOU, Haixun WANG, Diane COOK, Jian PEI, Wei WANG, Osmar ZAÏANE, Xindong WU
Place of PublicationLos Alamitos
ISBN (Print)9781467300056
Publication statusPublished - 2011


Lu, 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


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