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.

翻译：本文提出一个新的基于字典的不受监督的情感分析方法, 用于测量2022年乌克兰-俄罗斯冲突的美元和美元。 $\ textit{Reddit. com}$\\ textit{Reddit. com}$是人类在冲突前三个月对日常事件的反应的主要来源。 有关乌克兰和新闻的六种不同子编辑( 乌克兰、 世界新闻、 Ukraina、 乌克兰冲突、 乌克兰战争展望报告、 乌克兰战争报告 、 乌克兰战争报告 ) 及其相关评论被废弃, 并创建数据集 。 在这个剧中, 多重分析, 如 (1) 公众利益, (2) 希望/ 种子得分, (3) 股票价格互动被使用。 我们推广一种字典方法, 给每个用户都带来希望。 Litetent Dirichlet 分配(LDA) 主题模型的算法也被用来理解用户提出的主要问题和关键谈话点 。 实验分析显示, Azov/ 和Severformais 事件之后, 的象征和战略损失在目前、 和战争中都具有积极意义。