An entropy-based evaluation for sentiment analysis of stock market prices using Twitter data

Andreas Kanavos, Gerasimos Vonitsanos, Alaa Mohasseb, Phivos Mylonas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Stock markets prediction is considered a considerably demanding task due to its notable returns as well as due to the high randomness within the stock market. Moreover, stock price alternations are primarily related to the capital circumstances and hot occasions/events. Nowadays, researchers have sufficiently improved prediction accuracy by taking into consideration news and social media. However, the existing strategies do not employ the different impacts that events may pose. Streaming data proves to be a perpetual real-time source of data analysis as information from different web sources can be carried. In this paper, we explore whether estimations, in terms of sentiment analysis derived from Twitter posts, can be correlated to the stock market prices. Initially, the daily Twitter posts are analyzed and different n-grams along with two strategies that are utilized to increase the accuracy of the classification, are applied. Spark streaming has been employed for the processing of Twitter data, while Apache Flume has been utilized for the analysis.
Original languageEnglish
Title of host publicationProceedings of 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages7
DOIs
Publication statusPublished - 9 Nov 2020
Event15th International Workshop on Semantic and Social Media Adaptation and Personalization - Virtual Event, Zakynthos, Greece
Duration: 29 Oct 202030 Oct 2020

Workshop

Workshop15th International Workshop on Semantic and Social Media Adaptation and Personalization
Country/TerritoryGreece
CityZakynthos
Period29/10/2030/10/20

Keywords

  • stock market
  • social networks
  • sentiment analysis
  • spark streaming
  • Apache Flume
  • Apache Cassandra

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