摘 要
随着信息技术的迅猛发展，自然语言处理技术在社会各领域的应用日益广泛，其中文本情感分析作为理解人类情感和行为的重要工具，已成为学术界和工业界的热点研究方向本研究以提升文本情感分析的准确性和效率为目标，系统探讨了基于深度学习的情感分析模型及其优化策略首先，针对传统方法在特征提取和语义理解上的局限性，引入了预训练语言模型如BERT和GPT，并结合领域特定数据进行微调，显著增强了模型对复杂情感模式的捕捉能力其次，提出了一种融合多模态信息的情感分析框架，通过整合文本、图像和音频等多源数据，有效提升了跨模态情感识别的性能此外，为解决标注数据不足的问题，设计了一种基于弱监督学习的半自动化标注方法，大幅降低了人工成本实验结果表明，所提出的模型在多个公开数据集上取得了优于现有方法的表现，特别是在细粒度情感分类任务中展现了突出优势最后，本研究总结了当前文本情感分析技术的主要挑战，并对未来发展方向进行了展望，包括增强模型可解释性、拓展应用场景以及探索更高效的训练策略总而言之，本研究不仅为文本情感分析提供了新的思路和技术手段，还为进一步推动自然语言处理技术的实际应用奠定了坚实基础
关键词：文本情感分析；深度学习；预训练语言模型；多模态信息融合；弱监督学习
ABSTRACT
With the rapid development of information technology, natural language processing techniques have been increasingly applied across various social domains. Among these applications, text sentiment analysis has emerged as a crucial tool for understanding human emotions and behaviors, becoming a focal research direction in both academia and industry. This study aims to enhance the accuracy and efficiency of text sentiment analysis by systematically exploring deep learning-based sentiment analysis models and their optimization strategies. First, to address the limitations of traditional methods in feature extraction and semantic understanding, pre-trained language models such as BERT and GPT were introduced and fine-tuned with domain-specific data, significantly improving the model's ability to capture complex emotional patterns. Second, a multimodal information fusion fr amework for sentiment analysis was proposed, which integrates multi-source data including text, images, and audio, effectively enhancing the performance of cross-modal emotion recognition. Furthermore, to tackle the issue of insufficient labeled data, a semi-automated labeling method based on weakly supervised learning was designed, substantially reducing manual annotation costs. Experimental results demonstrate that the proposed models outperform existing methods on multiple public datasets, particularly showing significant advantages in fine-grained sentiment classification tasks. Finally, this study summarizes the primary challenges of current text sentiment analysis technologies and provides an outlook on future research directions, including enhancing model interpretability, expanding application scenarios, and exploring more efficient training strategies. In summary, this research not only offers new perspectives and technical approaches for text sentiment analysis but also lays a solid foundation for further advancing the practical applications of natural language processing technologies.
Keywords: Text Sentiment Analysis; Deep Learning; Pretrained Language Model; Multimodal Information Fusion; Weakly Supervised Learning
目 录
摘 要 I
ABSTRACT II
引言 1
第1章 文本情感分析基础理论 2
1.1 情感分析的定义与范畴 2
1.2 自然语言处理的核心任务 2
1.3 情感分析的技术发展历程 3
1.4 当前研究的主要挑战 3
第2章 情感分析的关键技术方法 5
2.1 基于规则的情感分析方法 5
2.2 机器学习在情感分析中的应用 5
2.3 深度学习模型的创新实践 6
2.4 预训练语言模型的作用分析 6
2.5 技术方法的对比与选择 6
第3章 情感分析的数据与特征处理 8
3.1 数据采集与预处理策略 8
3.2 特征提取与表示方法研究 8
3.3 情感词典的构建与优化 9
3.4 跨领域数据的适配问题 9
3.5 数据质量对结果的影响 10
第4章 情感分析的实际应用场景 11
4.1 社交媒体文本的情感挖掘 11
4.2 商业评论的情感趋势分析 11
4.3 医疗健康领域的情感监测 12
4.4 跨语言情感分析的技术实现 12
4.5 实际应用中的伦理与隐私问题 12
结论 14
参考文献 15
致 谢 16
