Using Arbiter and Combiner Tree to Classify Contexts of Data

Tawunrat Chalothorn, J. Ellman
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引用次数: 4

Abstract

This paper reports on the use of ensemble learning to classify as either positive or negative the sentiment of Tweets. Tweets were chosen as Twitter is a popular tool and a public, human annotated dataset was made available as part of the SemEval 2013 competition. We report on a classification approach that contrasts single machine learning algorithms with a combination of algorithms in an ensemble learning approach. The single machine learning algorithms used were support vector machine (SVM) and Naive Bayes (NB), while the methods of ensemble learning include the arbiter tree and the combiner tree. Our system achieved an F-score using Tweets and SMS with the arbiter tree at 83.57% and 93.55%, respectively, which was better than base classifiers; meanwhile, the results from the combiner tree achieved lower scores than base classifiers.
用仲裁者和组合树对数据上下文进行分类
本文报道了使用集成学习将推文的情绪分类为积极或消极。Twitter之所以被选中,是因为Twitter是一种流行的工具,而且作为SemEval 2013竞赛的一部分,Twitter提供了一个公开的、人类注释的数据集。我们报告了一种分类方法,该方法将单个机器学习算法与集成学习方法中的算法组合进行了对比。采用的单机学习算法有支持向量机(SVM)和朴素贝叶斯(NB),集成学习方法有仲裁者树和组合树。我们的系统在使用tweet和SMS时分别获得了83.57%和93.55%的f分,优于基本分类器;同时,组合树的结果比基分类器的得分低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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