Multi-Entity Aspect-Based Sentiment Analysis With Context, Entity and Aspect Memory

Authors

  • Jun Yang Nanjing University
  • Runqi Yang Nanjing University
  • Chongjun Wang Nanjing University
  • Junyuan Xie Nanjing University

Keywords:

Multi-Entity Aspect-Based Sentiment Analysis, Opinion Mining

Abstract

Inspired by recent works in Aspect-Based Sentiment Analysis (ABSA) on product reviews and faced with more complex posts on social media platforms mentioning multiple entities as well as multiple aspects, we define a novel task called Multi-Entity Aspect-Based Sentiment Analysis (ME-ABSA). This task aims at fine-grained sentiment analysis of (entity, aspect) combinations, making the well-studied ABSA task a special case of it. To address the task, we propose an innovative method that models Context memory, Entity memory and Aspect memory, called CEA method. Our experimental results show that our CEA method achieves a significant gain over several baselines, including the state-of-the-art method for the ABSA task, and their enhanced versions, on datasets for ME-ABSA and ABSA tasks. The in-depth analysis illustrates the significant advantage of the CEA method over baseline methods for several hard-to-predict post types. Furthermore, we show that the CEA method is capable of generalizing to new (entity, aspect) combinations with little loss of accuracy. This observation indicates that data annotation in real applications can be largely simplified.

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Published

2018-04-26

How to Cite

Yang, J., Yang, R., Wang, C., & Xie, J. (2018). Multi-Entity Aspect-Based Sentiment Analysis With Context, Entity and Aspect Memory. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12059