Sentiment Analysis for Measuring Hope and Fear from Reddit Posts During the 2022 Russo-Ukrainian Conflict

TL;DR

This study introduces a novel lexicon-based unsupervised sentiment analysis method to measure hope and fear from Reddit posts during the 2022 Russo-Ukrainian conflict, revealing emotional trends linked to key events.

Contribution

It develops a new dictionary-based approach combined with LDA topic modeling to analyze public sentiment and interest on Reddit during a major conflict.

Findings

Hope decreases after strategic losses like Azovstal and Severodonetsk

Hope and fear spike after key battles and non-military events

Public interest and sentiment fluctuate with conflict developments

Abstract

This paper proposes a novel lexicon-based unsupervised sentimental analysis method to measure the $``\textit{hope}"$ and $``\textit{fear}"$ for the 2022 Ukrainian-Russian Conflict. $\textit{Reddit.com}$ is utilised as the main source of human reactions to daily events during nearly the first three months of the conflict. The top 50 $``hot"$ posts of six different subreddits about Ukraine and news (Ukraine, worldnews, Ukraina, UkrainianConflict, UkraineWarVideoReport, UkraineWarReports) and their relative comments are scraped and a data set is created. On this corpus, multiple analyses such as (1) public interest, (2) hope/fear score, (3) stock price interaction are employed. We promote using a dictionary approach, which scores the hopefulness of every submitted user post. The Latent Dirichlet Allocation (LDA) algorithm of topic modelling is also utilised 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 after important battles, but also some non-military events, such as Eurovision and football games.