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A Joint Model for Sentiment Classification and Opinion Words Extraction

  • Dawei Cong
  • Jianhua Yuan
  • Yanyan Zhao
  • Bing Qin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11221)

Abstract

In recent years, mining opinions from customer reviews has been widely explored. Aspect-level sentiment analysis is a fine-grained subtask, which aims to detect the sentiment polarity towards a particular target in a sentence. While most previous works focus on sentiment polarity classification, opinion words towards the target are also very important for that they provide details about target and contribute to judging polarity. To this end, we propose a hierarchical network for jointly modeling aspect-level sentiment classification and word-level opinion words extraction. Our joint model acquires superior performance in opinion words extraction and achieves comparable results in sentiment polarity classification on two datasets from SemEval 2014.

Keywords

Aspect-level sentiment analysis Opinion words extraction Neural network Attention mechanism 

Notes

Acknowledgements

We thank the anonymous reviewers for their valuable suggestions. This work was supported by the National Natural Science Foundation of China (NSFC) via grant 61632011, 61772153 and 71490722.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dawei Cong
    • 1
  • Jianhua Yuan
    • 1
  • Yanyan Zhao
    • 2
  • Bing Qin
    • 1
  1. 1.Research Center for Social Computing and Information RetrievalHarbin Institute of TechnologyHarbinChina
  2. 2.Department of Media Technology and ArtHarbin Institute of TechnologyHarbinChina

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