Sentiment analysis for measuring hope and fear from Reddit posts during the 2022 Russo-Ukrainian conflict

Guerra, Alessio and Karakuş, Oktay ORCID: https://orcid.org/0000-0001-8009-9319 2023. Sentiment analysis for measuring hope and fear from Reddit posts during the 2022 Russo-Ukrainian conflict. Frontiers in Artificial Intelligence 6 , 1163577. 10.3389/frai.2023.1163577

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Abstract

This article proposes a novel lexicon-based unsupervised sentiment analysis method to measure the “hope” and “fear” for the 2022 Ukrainian-Russian Conflict. Reddit.com is utilized as the main source of human reactions to daily events during nearly the first 3 months of the conflict. The top 50 “hot” posts of six different subreddits about Ukraine and news (Ukraine, worldnews, Ukraina, UkrainianConflict, UkraineWarVideoReport, and UkraineWarReports) along with their relative comments are scraped every day between 10th of May and 28th of July, and a novel data set is created. On this corpus, multiple analyzes, such as (1) public interest, (2) Hope/Fear score, and (3) stock price interaction, are employed. We use a dictionary approach, which scores the hopefulness of every submitted user post. The Latent Dirichlet Allocation (LDA) algorithm of topic modeling is also utilized to understand the main issues raised by users and what are the key talking points. Experimental analysis shows that the hope strongly decreases after the symbolic and strategic losses of Azovstal (Mariupol) and Severodonetsk. Spikes in hope/fear, both positives and negatives, are present not only after important battles, but also after some non-military events, such as Eurovision and football games.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Frontiers Media
Date of First Compliant Deposit:	20 April 2023
Date of Acceptance:	7 March 2023
Last Modified:	08 Jul 2023 00:41
URI:	https://orca.cardiff.ac.uk/id/eprint/158952

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