  Sentiment analysis, an increasingly vital field in both academia and
industry, plays a pivotal role in machine learning applications, particularly
on social media platforms like Reddit. However, the efficacy of sentiment
analysis models is hindered by the lack of expansive and fine-grained emotion
datasets. To address this gap, our study leverages the GoEmotions dataset,
comprising a diverse range of emotions, to evaluate sentiment analysis methods
across a substantial corpus of 58,000 comments. Distinguished from prior
studies by the Google team, which limited their analysis to only two models,
our research expands the scope by evaluating a diverse array of models. We
investigate the performance of traditional classifiers such as Naive Bayes and
Support Vector Machines (SVM), as well as state-of-the-art transformer-based
models including BERT, RoBERTa, and GPT. Furthermore, our evaluation criteria
extend beyond accuracy to encompass nuanced assessments, including hierarchical
classification based on varying levels of granularity in emotion
categorization. Additionally, considerations such as computational efficiency
are incorporated to provide a comprehensive evaluation framework. Our findings
reveal that the RoBERTa model consistently outperforms the baseline models,
demonstrating superior accuracy in fine-grained sentiment classification tasks.
This underscores the substantial potential and significance of the RoBERTa
model in advancing sentiment analysis capabilities.
