Tweet sentiment analysis with classifier ensembles
Tweet sentiment analysis with classifier ensembles
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DOI：
10.1016/j.dss.2014.07.003
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发表时间：
2014-10-01
影响因子：
7.5
通讯作者：
Hruschka, Estevam R., Jr.
中科院分区：
文献类型：
--
作者：
da Silva, Nadia F. F.;Hruschka, Eduardo R.;Hruschka, Estevam R., Jr.
Twitter是一个微型博客网站，用户可以在其中向朋友（追随者）发布更新（tweets）。它已经成为所谓情绪的巨大数据集。在本文中，我们介绍了一种方法，自动分类的情绪推文使用分类器集成和词汇。推文被分类为关于查询词的正面或负面。这种方法对于可以使用情感分析来搜索产品的消费者，旨在监控其品牌的公众情感的公司以及许多其他应用程序都很有用。事实上，微博服务中的情感分类（例如，Twitter）通过分类器集合和词汇的研究还没有在文献中得到很好的探讨。我们在各种公共推文情感数据集上的实验表明，由多项式朴素贝叶斯，SVM，随机森林和Logistic回归形成的分类器集成可以提高分类精度。(C)2014爱思唯尔有限公司版权所有。
Twitter is a microblogging site in which users can post updates (tweets) to friends (followers). It has become an immense dataset of the so-called sentiments. In this paper, we introduce an approach that automatically classifies the sentiment of tweets by using classifier ensembles and lexicons. Tweets are classified as either positive or negative concerning a query term. This approach is useful for consumers who can use sentiment analysis to search for products, for companies that aim at monitoring the public sentiment of their brands, and for many other applications. Indeed, sentiment classification in microblogging services (e.g., Twitter) through classifier ensembles and lexicons has not been well explored in the literature. Our experiments on a variety of public tweet sentiment datasets show that classifier ensembles formed by Multinomial Naive Bayes, SVM, Random Forest, and Logistic Regression can improve classification accuracy. (C) 2014 Elsevier B.V. All rights reserved.