BiverWordle: Visualizing Stock Market Sentiment with Financial Text Data and Trends

doi:10.1145/3615522.3615541

Details of Research Outputs

Title	BiverWordle: Visualizing Stock Market Sentiment with Financial Text Data and Trends
Creator	Xia，Lei 1; Gao，Yi Ping 1; Lin，Le 1; Chen，Yu Xi 1; Zhang，Kang 2
Date Issued	2023-09-22
Source Publication	ACM International Conference Proceeding Series
Abstract	While financial forums are increasingly significant in financial analysis, current visualization tools do not properly exploit their text data. To address this, we present BiverWordle, a novel tool that reveals the relationship between market sentiment and firm trends. BiverWordle integrates candlestick chart, ThemeRiver, and Wordle with text classification and sentiment analysis techniques to decode market dynamics from textual sources, such as shareholder opinions and firm announcements. With the application of a Voting model to the manually labeled data, we achieved an accuracy of approximately 64%. BiverWordle facilitates the extraction of shareholder insights from sparse comments and provides a visual method for historical stock trend analysis, which we validated with three distinct stock trends. Resources are accessible at https://github.com/Brian-Lei-XIA/BiverWordle.
Keyword	Financial Text Processing Sentiment Analysis Visualization
DOI	10.1145/3615522.3615541
URL	View source
Language	英语English
Scopus ID	2-s2.0-85178381174
Citation statistics
Document Type	Conference paper
Identifier	http://repository.uic.edu.cn/handle/39GCC9TT/11570
Collection	Beijing Normal-Hong Kong Baptist University
Affiliation	1.Department of Computer Science,Beijing Normal University,Hong Kong Baptist University,United International College,Zhuhai,China,China 2.Computational Media and Arts,Hong Kong University of Science and Technology,Guangzhou,China
First Author Affilication	Beijing Normal-Hong Kong Baptist University
Recommended Citation GB/T 7714	Xia，Lei,Gao，Yi Ping,Lin，Leet al. BiverWordle: Visualizing Stock Market Sentiment with Financial Text Data and Trends[C], 2023.

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