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

This paper delves into enhancing the classification performance on the
GoEmotions dataset, a large, manually annotated dataset for emotion detection
in text. The primary goal of this paper is to address the challenges of
detecting subtle emotions in text, a complex issue in Natural Language
Processing (NLP) with significant practical applications. The findings offer
valuable insights into addressing the challenges of emotion detection in text
and suggest directions for future research, including the potential for a
survey paper that synthesizes methods and performances across various datasets
in this domain.

本研究旨在提高文本情感分类在 GoEmotions 数据集上的性能，解决在自然语言处理中检测微妙情感的挑战，并为未来研究提供有价值的见解和方向，包括合成该领域不同数据集上的方法和性能的综述论文的潜力。

使用数据增强和迁移学习在细粒度情感检测数据集上的大型语言模型

Large Language Models on Fine-grained Emotion Detection Dataset with  Data Augmentation and Transfer Learning

We present a new large-scale emotion-labeled symbolic music dataset
consisting of 12k MIDI songs. To create this dataset, we first trained emotion
classification models on the GoEmotions dataset, achieving state-of-the-art
results with a model half the size of the baseline. We then applied these
models to lyrics from two large-scale MIDI datasets. Our dataset covers a wide
range of fine-grained emotions, providing a valuable resource to explore the
connection between music and emotions and, especially, to develop models that
can generate music based on specific emotions. Our code for inference, trained
models, and datasets are available online.

我们提出了一个新的大规模情感标记的符号音乐数据集，该数据集包括 12k 首 MIDI 音乐。通过在 GoEmotions 数据集上训练情感分类模型，我们实现了半基线模型大小的先进结果。然后，我们将这些模型应用到两个大规模 MIDI 数据集的歌词上。我们的数据集涵盖了广泛的细粒度情感，为探究音乐与情感之间的联系以及基于特定情感生成音乐的模型的发展提供了宝贵的资源。我们的推断代码、训练模型和数据集在网上可获得。