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.

使用 GoEmotions 数据集评估情感分析模型的效能并扩展研究范围，发现 RoBERTa 模型在细粒度情感分类任务上表现出色，推动了情感分析能力的发展。

使用机器学习和自然语言处理方法进行情感分析中 Reddit 评论的性能评估

Performance evaluation of Reddit Comments using Machine Learning and  Natural Language Processing methods in Sentiment Analysis

Emotion datasets used for Speech Emotion Recognition (SER) often contain
acted or elicited speech, limiting their applicability in real-world scenarios.
In this work, we used the Emotional Voice Messages (EMOVOME) database,
including spontaneous voice messages from conversations of 100 Spanish speakers
on a messaging app, labeled in continuous and discrete emotions by expert and
non-expert annotators. We created speaker-independent SER models using the
eGeMAPS features, transformer-based models and their combination. We compared
the results with reference databases and analyzed the influence of annotators
and gender fairness. The pre-trained Unispeech-L model and its combination with
eGeMAPS achieved the highest results, with 61.64% and 55.57% Unweighted
Accuracy (UA) for 3-class valence and arousal prediction respectively, a 10%
improvement over baseline models. For the emotion categories, 42.58% UA was
obtained. EMOVOME performed lower than the acted RAVDESS database. The elicited
IEMOCAP database also outperformed EMOVOME in the prediction of emotion
categories, while similar results were obtained in valence and arousal.
Additionally, EMOVOME outcomes varied with annotator labels, showing superior
results and better fairness when combining expert and non-expert annotations.
This study significantly contributes to the evaluation of SER models in
real-life situations, advancing in the development of applications for
analyzing spontaneous voice messages.

使用来自西班牙 100 位说话者之间的即兴语音消息的情感数据集（EMOVOME 数据库），利用 eGeMAPS 特征、基于 Transformer 的模型及其组合，创建了基于说话者的独立 SER 模型，对连续和离散情感进行标注，并比较参考数据库的结果，并分析注释者和性别公平性的影响。使用预训练的 Unispeech-L 模型及其与 eGeMAPS 的组合取得了最高的结果，对于 3 类情感价值和唤醒度的预测，分别达到了 61.64% 和 55.57% 的非加权准确率（UA），相比基线模型提高了 10%。对于情感类别，获得了 42.58% 的 UA。EMOVOME 在情感类别的预测上不及被操纵的 RAVDESS 数据库，在情感价值和唤醒度方面与被引发的 IEMOCAP 数据库效果相似。此外，EMOVOME 的结果随着注释者标签的变化而变化，当结合专家和非专家的注释时，表现出更好的结果和更好的公平性。该研究在评估 SER 模型在现实生活中的应用方面做出了重要贡献，推动了用于分析即兴语音消息的应用程序的开发。