Sentiment analysis for measuring hope and fear from Reddit posts during the 2022 Russo-Ukrainian conflict - Guerra, Alessio; Karakuş, Oktay (Frontiers in Artificial Intelligence, 2023)
This paper proposes a novel lexicon-based unsupervised sentimental analysis method to measure the ``hope” and ``fear" for the 2022 Ukrainian-Russian Conflict. 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) along with their relative comments are scraped every day between 10th May and 28th July, and data set is created. On this corpus, multiple analyses 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 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.