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. $\textit{this http URL}$ 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.

该论文提出了一种新的基于字典的无监督情感分析方法，用于衡量 2022 年乌克兰俄罗斯冲突中的 “希望” 和 “恐惧”，并使用 Reddit 搭建数据集进行多项分析，利用主题模型算法 LDA 来理解用户提出的主要问题和要点。实验结果表明，随着 Azovstal（Mariupol）和 Severodonetsk 的象征性和战略性损失，希望强烈下降。此外，重要战斗和一些非军事事件（如欧洲歌唱比赛和足球比赛）均存在希望 / 恐惧的大幅波动。

2022 年俄乌冲突期间从 Reddit 帖子中测量希望和恐惧的情感分析

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

There is a vast amount of data generated every second due to the rapidly
growing technology in the current world. This area of research attempts to
determine the feelings or opinions of people on social media posts. The dataset
we used was a multi-source dataset from the comment section of various social
networking sites like Twitter, Reddit, etc. Natural Language Processing
Techniques were employed to perform sentiment analysis on the obtained dataset.
In this paper, we provide a comparative analysis using techniques of
lexicon-based, machine learning and deep learning approaches. The Machine
Learning algorithm used in this work is Naive Bayes, the Lexicon-based approach
used in this work is TextBlob, and the deep-learning algorithm used in this
work is LSTM.

本文使用多种技术，如基于词典的、机器学习和深度学习方法，对社交媒体上的评论数据进行情感分析，并提供了比较分析结果。在本研究中，我们使用了来自 Twitter、Reddit 等社交网络网站的评论等多源数据集。选择了朴素贝叶斯机器学习算法、TextBlob 词典方法和 LSTM 深度学习算法。

多源社交媒体情感分析的比较研究

Comparative Study of Sentiment Analysis for Multi-Sourced Social Media Platforms

In the presented study, we discover that the so-called "transition freedom"
metric appears superior for unsupervised tokenization purposes in comparison to
statistical metrics such as mutual information and conditional probability,
providing F-measure scores in range from 0.71 to 1.0 across explored
multilingual corpora. We find that different languages require different
offshoots of that metric (such as derivative, variance, and "peak values") for
successful tokenization. Larger training corpora do not necessarily result in
better tokenization quality, while compressing the models by eliminating
statistically weak evidence tends to improve performance. The proposed
unsupervised tokenization technique provides quality better than or comparable
to lexicon-based ones, depending on the language.

本研究发现，相比于互信息和条件概率等统计度量方法，所谓的 “过渡自由度” 度量方法在无监督分词方面表现更佳，跨多语料库提供了 0.71 到 1.0 的 F - 度量分数。我们发现，不同的语言需要该度量方法的不同分支（如导数、方差和 “峰值”）才能实现成功的分词。 更大的训练语料库不一定会导致更好的分词质量，而通过消除统计上薄弱的证据来压缩模型往往会提高性能。 该无监督分词技术提供的质量优于或与基于词典的技术相媲美，具体效果取决于语言。