  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.

Computation and Language;Computers and Society