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Sentiment Embedded Semantic Space for More Accurate Sentiment Analysis

  • Jianguo Jiang
  • Yue Lu
  • Min Yu
  • Gang Li
  • Chao Liu
  • Weiqing Huang
  • Fangtao Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)

Abstract

Word embedding is one common word vector representation with improved performance for sentiment analysis task. Most existing methods of learning context-based word embedding are semantic oriented, but they typically fail to capture the sentiment information. This may result in words with similar vectors but with very different sentiment polarities, thus degrading the followed sentiment analysis performance. In this paper, we propose a novel and efficient method to yield the Sentiment Embedded Semantic Space that captures the connection between the sentiment space and the semantic space. The proposed method is based on K-means and CNN. In addition, we develop a more fine-grained sentiment dictionary based on HowNet Dictionary together with the processing dataset. Extensive experiments on benchmark datasets show that the proposed method leads to more accurate sentiment classifier and reduces the task-specific word embedding effort.

Keywords

Sentiment space Sentiment analysis K-means CNN 

Notes

Acknowledgment

This work is supported by the National Key Research and Development Program of China (2016YFB0801001, 2016YFB0801004), the International Cooperation Project of Institute of Information Engineering, Chinese Academy of Sciences under Grant No. Y7Z0511101 and Key Lab of Information Network Security, Ministry of Public Security (No. C17614).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.School of Information TechnologyDeakin UniversityGeelongAustralia

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