使用机器学习方法进行新闻的情感自动分类

本文主要研究机器学习方法在新闻文本的情感分类中的应用,判断其是正面还是负面。我们利用朴素贝叶斯和最大熵方法进行新闻及评论语料的情感分类研究。实验表明,机器学习方法在基于情感的文本分类中也能取得不错的分类性能,最高准确率能达到90%。同时我们也发现,对于基于情感的文本分类,选择具有语义倾向的词汇作为特征项、对否定词正确处理和采用二值作为特征项权重能提高分类的准确率。总之,基于情感的文本分类是一个更具挑战性的工作。

Sentiment Classification for Chinese News Using Machine Learning Methods

In this paper,we study how to apply machine learning techniques to solve sentiment classification problems.The main task of sentiment classification is to determine whether news or reviews is negative or positive.Naive Bayes and Maximum Entropy classification are used for the sentiment classification of Chinese news and reviews.The experimental results show that the methods we employed perform well.The accuracy of classification can achieve about 90%.Moreover,we find that selecting the words with polarity as features,negation tagging and representing test documents as feature presence vectors can improve the performance of sentiment classification.Conclusively,sentiment classification is a more challenging problem.