Extracting Verb Expressions Implying Negative Opinions


  • Huayi Li University of Illinois at Chicago
  • Arjun Mukherjee University of Houston
  • Jianfeng Si Institute for Infocomm Research
  • Bing Liu University of Illinois at Chicago




Verb Expression, Opinion Mining


Identifying aspect-based opinions has been studied extensively in recent years. However, existing work primarily focused on adjective, adverb, and noun expressions. Clearly, verb expressions can imply opinions too. We found that in many domains verb expressions can be even more important to applications because they often describe major issues of products or services. These issues enable brands and businesses to directly improve their products or services. To the best of our knowledge, this problem has not received much attention in the literature. In this paper, we make an attempt to solve this problem. Our proposed method first extracts verb expressions from reviews and then employs Markov Networks to model rich linguistic features and long distance relationships to identify negative issue expressions. Since our training data is obtained from titles of reviews whose labels are automatically inferred from review ratings, our approach is applicable to any domain without manual involvement. Experimental results using real-life review datasets show that our approach outperforms strong baselines.




How to Cite

Li, H., Mukherjee, A., Si, J., & Liu, B. (2015). Extracting Verb Expressions Implying Negative Opinions. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9525