摘 要
随着大数据时代的到来，海量文本数据中蕴含的情感信息成为学术界和工业界关注的焦点。情感分析作为自然语言处理领域的重要分支，在舆情监控、用户反馈分析及市场预测等方面具有广泛的应用价值。然而，传统方法在面对复杂语义结构和大规模数据时存在局限性。为此，本研究提出了一种基于深度学习的情感分析框架，旨在通过神经网络模型挖掘大数据中的情感特征并提升分析精度。具体而言，研究采用了双向长短时记忆网络（BiLSTM）结合注意力机制（Attention Mechanism）的架构，以捕捉文本中的长距离依赖关系和关键情感信息。同时，引入预训练词向量与自适应学习率优化算法，进一步增强模型的表达能力和收敛效率。实验结果表明，该方法在多个公开数据集上取得了优于传统机器学习方法和单一深度学习模型的表现，特别是在处理多类别情感分类任务时展现出显著优势。
关键词：情感分析 深度学习 双向长短时记忆网络
Abstract
With the advent of the era of big data, the emotional information contained in massive text data has become the focus of academia and industry. As an important branch in the field of natural language processing, emotion analysis has a wide range of application value in public opinion monitoring, user feedback analysis and market prediction. However, traditional methods have limitations in facing complex semantic structures and large-scale data. To this end, this study proposes a deep learning-based emotion analysis fr amework, aiming to mine the emotion features in big data and improve the analysis accuracy through the neural network model. Specifically, the study adopted the architecture of bidirectional long-short memory network (BiLSTM) combined with attention mechanism (Attention Mechanism) to capture long-distance dependencies and key emotional information in the text. At the same time, the pre-trained word vector and adaptive learning rate optimization algorithm are introduced to further enhance the ex pression ability and convergence efficiency of the model. Experimental results show that the proposed method outperforms traditional machine learning methods and a single deep learning model on multiple publicly available datasets, especially showing significant advantages in handling multi-category emotion classification tasks.
Keyword:Sentiment Analysis Deep Learning Bidirectional Long Short-Term Memory Network
目 录
1绪论 1
1.1深度学习与情感分析的研究背景 1
1.2大数据时代情感分析的意义 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2深度学习在情感分析中的理论基础 2
2.1深度学习的基本原理概述 2
2.2常见深度学习模型及其特点 3
2.3情感分析的数学建模方法 3
2.4深度学习与传统方法的对比分析 3
2.5理论框架对实际应用的指导意义 4
3大数据环境下的情感分析关键技术 4
3.1大数据处理与存储技术 4
3.2数据预处理与特征提取方法 5
3.3深度学习模型的选择与优化 5
3.4情感分类算法的设计与实现 6
3.5技术难点及解决方案探讨 6
4深度学习驱动的情感分析案例研究 7
4.1社交媒体文本的情感分析实践 7
4.2商品评论数据的情感挖掘实验 7
4.3跨领域情感分析的挑战与应对策略 8
4.4模型性能评估与结果分析 8
4.5实验结论与未来改进方向 9
结论 9
参考文献 11
致谢 12
