基于机器学习的文本情感多分类的学习与研究

Study and Research on Text Emotion Multi-Classification Based on Machine Learning

文本分类与情感分类是自然语言处理中基础的领域,为帮助初学者对文本情感多分类的项目学习,在机器学习的基础上,分析了线性逻辑回归算法、朴素贝叶斯模型在文本情感分类项目中的应用,并针对数据处理、模型构建、模型训练、模型测试过程中初学者难以解决和易出错的部分进行分析与实现。结合kaggle上的比赛数据实例,实现了完整的文本情感多分类项目并做出详细分析,项目评测结果较为可观,证实可以帮助初学者更易上手文本情感多分类和机器学习。同时提出了基于传统二分类问题的多分类问题解决方法。

Text categorization and emotion classification are basic fieldsin natural language processing.To help beginners learn the items of text sentiment multi-classification,based on machine learning,the linear logistic regression algorithm and Bayesian model are analyzed in the text sentiment classification project.In the process of data processing、model building、model training and mod⁃el testing,it is difficult for beginners to solve and error-prone parts are analyzed and implemented.Combined with the game data examples on Kaggle,a complete text emotion multi-classification project has been implemented and detailed analysis has been made.The results are considerable,which proves that it can help beginners get started with text emotion classification and machine learning.At the same time,a multi-classification problem solving method based on the traditional two-classification problem is proposed.