题目：基于机器学习的文本分类方法研究
院（系、部）：计算机科学与技术学院
专业：计算机科学与技术
班 级：2020级3班
指导教师：张三
摘要
文本分类是自然语言处理中的一个重要任务，广泛应用于垃圾邮件过滤、情感分析、话题检测等领域。随着机器学习技术的发展，基于机器学习的文本分类方法逐渐成为研究热点。本文综述了几种常用的文本分类方法，包括朴素贝叶斯、支持向量机、k近邻算法和深度学习方法，并通过实验比较了不同方法在文本分类任务中的性能。研究表明，深度学习方法在处理大规模数据时具有显著优势。本文的研究结果为进一步优化文本分类算法提供了参考。
关键词：文本分类；机器学习；朴素贝叶斯；支持向量机；深度学习
Abstract
Text classification is a significant task in natural language processing, widely used in spam filtering, sentiment analysis, topic detection, and more. With the advancement of machine learning technology, machine learning-based text classification methods have gradually become a research hotspot. This paper reviews several commonly used text classification methods, including Naive Bayes, Support Vector Machine, k-Nearest Neighbors, and deep learning methods, and compares the performance of different methods in text classification tasks through experiments. The research shows that deep learning methods have significant advantages in handling large-scale data. The research results provide a reference for further optimization of text classification algorithms.