摘 要
随着信息技术的迅猛发展，文本数据呈爆炸式增长，如何从海量文本中挖掘有价值的信息成为亟待解决的问题。机器学习凭借其强大的模式识别和预测能力，在文本挖掘领域展现出巨大潜力。本研究旨在探讨机器学习算法在文本挖掘中的应用效果，以期为相关研究提供参考。研究选取了多种经典的机器学习算法，如支持向量机、朴素贝叶斯等，并引入深度学习算法构建对比实验。通过预处理文本数据集，采用特征提取技术将文本转化为可用于训练的特征向量，再利用上述算法进行分类、聚类等任务。结果表明，相较于传统方法，基于机器学习的文本挖掘准确率显著提高，特别是在情感分析、主题模型构建方面表现优异。创新点在于结合词嵌入与卷积神经网络优化文本表示，有效捕捉语义信息。此外，提出一种融合多源异构文本数据的混合模型，增强了模型的泛化能力。该研究不仅验证了机器学习在文本挖掘中的有效性，还为后续研究提供了新的思路与方法，对推动文本挖掘技术的发展具有重要意义。
关键词：机器学习 文本挖掘 深度学习
Abstract
With the rapid development of information technology, textual data has experienced explosive growth, making the extraction of valuable information from vast text corpora an urgent challenge. Machine learning, with its robust pattern recognition and predictive capabilities, demonstrates significant potential in text mining. This study investigates the application effects of machine learning algorithms in text mining to provide a reference for related research. Various classical machine learning algorithms, such as Support Vector Machines (SVM) and Naive Bayes, were selected, along with deep learning algorithms to construct comparative experiments. Text datasets were preprocessed, and feature extraction techniques were employed to transform texts into feature vectors suitable for training. These algorithms were then utilized for classification, clustering, and other tasks. The results indicate that machine learning-based text mining significantly improves accuracy compared to traditional methods, particularly in sentiment analysis and topic model construction. An innovation lies in combining word embeddings with Convolutional Neural Networks (CNN) to optimize text representation, effectively capturing semantic information. Additionally, a hybrid model integrating multi-source heterogeneous text data was proposed, enhancing the generalization ability of the model. This study not only validates the effectiveness of machine learning in text mining but also provides new insights and methodologies for future research, contributing significantly to the advancement of text mining technology.
Keyword:Machine Learning Text Mining Deep Learning
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 1
1.3本文研究方法 2
2文本预处理技术 2
2.1文本清洗与分词 2
2.2特征选择与降维 3
2.3数据标注与质量评估 3
3分类与聚类算法应用 4
3.1常用分类算法比较 4
3.2聚类算法在文本中的应用 4
3.3混合模型优化方案 5
4主题模型与语义分析 6
4.2词向量与语义表示 6
4.3情感分析技术应用 6
结论 7
参考文献 9
致谢 10
