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A Novel Way to Build Stock Market Sentiment Lexicon

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Data Science (ICDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1179))

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Abstract

The construction of domain-specific sentiment lexicon has become an important direction to improve the performance of sentiment analysis in recent years. As one of the important application areas of sentiment analysis, the stock market also has some related researches. However, when considering the heterogeneity of the stock market relative to other fields, these studies ignore the heterogeneity of the stock market under different market conditions. At the same time, the annotated corpus is also indispensable for these studies, but the annotated corpus, especially the social media corpus that is not standardized, domain-specific and large in volume, is very difficult to obtain, manually labeling or automatic labeling has certain limitations. Besides, in the evaluation of the stock market sentiment lexicon, it is still based on the general classification algorithm evaluation criteria, but ignores the final application purpose of the sentiment analysis in the stock market: helping the stock market participants make investment decisions, that is, to achieve the highest profit. To address those problems, this paper proposes an unsupervised new method of constructing the stock market sentiment lexicon which based on the heterogeneity of the stock market, and an evaluation method of stock market sentiment lexicon. Subsequently, we selected four commonly used Chinese sentiment dictionaries as benchmark lexicons, and verified the method with an unlabeled Eastmoney stock posting corpus containing 15,733,552 posts about 2400 Chinese A-share listed companies. Finally, under our lexicon evaluation framework which based on the portfolio annualized return, the stock market sentiment lexicon constructed in this paper has achieved the best performance.

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Author information

Authors and Affiliations

  1. School of Business Administration, Southwestern University of Finance and Economics, No. 555, Liutai Avenue, Wenjiang Zone, Chengdu, 611130, China

    Yangcheng Liu

  2. Department of Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Fawaz E. Alsaadi

Authors
  1. Yangcheng Liu
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  2. Fawaz E. Alsaadi
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Corresponding author

Correspondence to Yangcheng Liu .

Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Jing He

  2. University of Illinois at Chicago, Chicago, USA

    Philip S. Yu

  3. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA

    Yong Shi

  4. Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou, China

    Xingsen Li

  5. Ningbo University, Ningbo, China

    Zhijun Xie

  6. Deakin University, Burwood, VIC, Australia

    Guangyan Huang

  7. Department of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

    Jie Cao

  8. Nanjing University of Posts and Telecommunications, Nanjing, China

    Fu Xiao

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Liu, Y., Alsaadi, F.E. (2020). A Novel Way to Build Stock Market Sentiment Lexicon. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_34

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  • DOI: https://doi.org/10.1007/978-981-15-2810-1_34

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2809-5

  • Online ISBN: 978-981-15-2810-1

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