摘 要
随着互联网技术的迅猛发展，用户生成内容呈爆炸式增长，情感分析作为自然语言处理领域的重要研究方向，已成为理解文本情感倾向的关键工具本研究旨在设计与实现一种基于深度学习的情感分析系统，以提高情感分类的准确性和效率该系统采用卷积神经网络（CNN）和长短时记忆网络（LSTM）相结合的混合模型，充分利用CNN在局部特征提取方面的优势以及LSTM对长序列依赖关系的建模能力为解决传统方法中特征工程复杂且耗时的问题，本研究提出了一种端到端的深度学习框架，能够自动从原始文本中学习高层次特征此外，针对训练数据不足导致的过拟合问题，引入了迁移学习策略，通过预训练的语言模型进一步提升模型的泛化性能实验结果表明，所提出的系统在多个公开数据集上取得了优于现有方法的表现，特别是在小样本场景下表现尤为突出本研究的主要贡献在于提出了一种高效的情感分析解决方案，不仅显著提升了分类精度，还降低了对人工特征选择的依赖。
关键词：情感分析 深度学习 卷积神经网络 长短时记忆网络
Abstract
With the rapid development of Internet technology, The explosion of user-generated content, Emotion analysis as an important research direction in the field of natural language processing, This study aims to design and implement a system of emotion analysis based on deep learning, To improve the accuracy and efficiency of emotion classification, the system uses a mixed model of convolutional neural network (CNN) and long and short time memory network (LSTM), Taking full advantage of CNN in local feature extraction and the ability of LSTM to model long sequence dependence to solve the complex and time-consuming problem of feature engineering in traditional methods, This study proposes an end-to-end deep learning fr amework, Ability to automatically learn high-level features from the original text in addition, Regarding the overfitting problem caused by insufficient training data, Introduced a transfer-learning strategy, The experimental results of further improving the generalization performance of the model through the pre-trained language model show that, The proposed system achieves superior performance over existing methods on multiple publicly available datasets, The main contribution of this study is to propose an efficient solution for emotion analysis, Not only significantly improves the classification accuracy, Also reduced the dependence on artificial feature selection.
Keyword:Sentiment Analysis Deep Learning Convolutional Neural Network Long Short-Term Memory Network
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2深度学习在情感分析中的理论基础 2
2.1情感分析的基本概念 2
2.2深度学习模型的适用性分析 3
2.3常用深度学习算法综述 3
2.4数据集与特征提取技术 3
3情感分析系统的设计与实现 4
3.1系统架构设计原则 4
3.2深度学习模型的选择与优化 4
3.3数据预处理与标注方法 5
3.4系统功能模块划分 6
4实验验证与结果分析 6
4.1实验环境与数据准备 6
4.2性能评估指标体系 7
4.3实验结果对比分析 7
4.4系统性能优化策略 8
结论 8
参考文献 10
致谢 11
