Sentiment Analysis of News on the Stock Market

Huimin Zong (), Sen Wu () and Guiying Wei ()
Additional contact information
Huimin Zong: University of Science and Technology Beijing
Sen Wu: University of Science and Technology Beijing
Guiying Wei: University of Science and Technology Beijing

A chapter in LISS 2021, 2022, pp 284-296 from Springer

Abstract: Abstract Media sentiment in the stock market affects investors’ behaviors and the operation of the stock market. Meanwhile, the development of data mining and artificial intelligence makes it possible to study massive stock market news on the Internet. In order to study media sentiment in the stock market, this paper collected the stock market news of 42 constituent stocks of China Securities 100 index in 2018 from Sina News. Firstly, we constructed financial news sentiment classifier based on word2vec and support machine vector (SVM). Then, we created three new daily sentiment indexes of individual stock by using the classification results. Finally, linear regression models were built to explore their relationship with the logarithmic trading volume, turnover, yield and volatility of individual stocks. The accuracy of financial news sentiment classifier was 86%, and the regression models could explain the influence of stock market media sentiment on some indicators of stock market to some extent. It was concluded that the financial news sentiment classifier based on word2vec and SVM works well. In addition, choosing the appropriate method to convert the word vector into the text vector has a great impact on the model effect and the simple average method performed better in this paper. At the same time, media sentiment has effects on the stock market. The impacts of negative and positive sentiment on logarithmic trading volume and turnover are positive. The positive impact of neutral sentiment on turnover is weaker than that of positive and negative sentiment.

Keywords: Media sentiment; Stock market; Sentiment analysis; Word2vec; Support vector machine; Linear regression (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-16-8656-6_27

Ordering information: This item can be ordered from
http://www.springer.com/9789811686566

DOI: 10.1007/978-981-16-8656-6_27

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().