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Sentiment classification of micro-blog comments based on Randomforest algorithm

机译:基于Randomforest算法的微博评论情感分类

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Sentiment classificationofMicro-blogcommentsaims to distinguish consumers' attitudes towarda certain brand or an event. A great number of research studies related to sentiment analysishave been conducted based on machine learning methods, such as Support Vector Machine andNaive Bayes, to build the classifier, and some other research studies tried to build one based onemotional thesaurus. However, the rate of accuracy is yet to improve. In order to improve theaccuracy rate further, this thesis bases on the Randomforest algorithm to construct an emotionaltendency classifier to help the mobile phone service providers to further understand consumers'attitudes toward their brand. TheRandomforest algorithm is a bagging algorithm of the ensemblelearning with some weak classifiers,which works verywell in anti-noise and reducing the overfittingproblem. Finally, a classifier with an 83% accuracy rate for sentiment classification ofmobilephone brands on Micro-blog comments was built based on Randomforest by using R languagesoftware.
机译:微博评论的情感分类是指区分消费者对某个品牌或事件的态度。已经基于支持向量机和朴素贝叶斯等机器学习方法进行了很多与情感分析有关的研究,以建立分类器,而其他一些研究则试图基于这种方法来构建情感分类器。 r 情感词库。但是,准确率尚未提高。为了进一步提高准确率,本文基于Randomforest算法构建情感倾向分类器,以帮助手机服务提供商进一步了解消费者对其品牌的态度。随机森林算法是具有一些弱分类器的集成学习的一种装袋算法,在抗噪和减少过拟合问题方面效果很好。最终,使用R语言 r n软件,基于Randomforest构建了对微博评论上的手机品牌的情感分类准确率达到83%的分类器。

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