基于集成学习理论的文本情感分类

随着Web2.0的迅速发展,越来越多的用户乐于在互联网上分享自己的观点或体验。这类评论信息迅速膨胀,仅靠人工的方法难以应对网上海量信息的收集和处理,因此基于计算机的文本情感分类技术应运而生,并且研究的重点之一就是提高分类的精度。由于集成学习理论是提高分类精度的一种有效途径,并且已在许多领域显示出其优于单个分类器的良好性能,为此,提出基于集成学习理论的文本情感分类方法。实验结果显示三种常用的集成学习方法 Bagging、Boosting和Random Subspace对基础分类器的分类精度都有提高,并且在不同的基础分类器条件下,Random Subspace方法较Bagging和Boosting方法在统计意义上更优,以上结果进一步验证了集成学习理论在文本情感分类中应用的有效性。

Text Sentiment Classification Based on Ensemble Learning

With the development of Web 2.0,more and more users are happy to share their opinions and experiences on the internet.Subsequently,it is increasingly difficult for people to collect and process the huge information from the network.Therefore,text sentiment classification based on the computer is proposed to tackle this problem.And one of the most important research directions is to enhance the classification accuracy for text sentiment classification.In addition,ensemble learning is an effective approach to enhance the classification accuracy and has shown better performance than base classifiers in many fields.Based on these considerations,text sentiment classification based on ensemble learning is proposed to enhance the performance of classifiers.Experimental results reveal that three ensemble methods,i.e.,Bagging,Boosting and Random Subspace,enhance the classification accuracy of different base classifiers.Compared with Bagging and Boosting,Random Subspace gets more significant improvement of the classification accuracy.All these results demonstrate the effectiveness and feasibility of application of ensemble learning in text sentiment classification.