Análise de sentimentos no contexto do evento “Halving” do bitcoin

Abstract

This study aimed to analyze Reddit users' sentiments towards the Bitcoin halving event using Natural Language Processing (NLP) techniques. Over 15,000 comments were collected from the r/Bitcoin, r/CryptoCurrency, and r/Blockchain communities. After textual preprocessing, sentiment analysis was conducted using two supervised models: VADER, a lexicon-based approach, and RoBERTa, a pre-trained Transformer-based model. Additionally, an unsupervised clustering technique (K-Means) combined with TF-IDF vectorization was employed to identify recurring lexical patterns within the comments. The results indicated that the majority of comments had a neutral tone, and skeptical discourses were also present. A comparison of the sentiment analysis methods revealed significant differences, particularly due to VADER's sensitivity to positive words even when taken out of context. The clustering identified three distinct groups, confirming that despite lexical differences, the overall discourse remained predominantly neaultral. This study highlights the value of combining supervised and unsupervised approaches to achieve a comprehensive understanding of public perceptions regarding economically significant events in the cryptocurrency landscape.