Efficient Twitter Sentiment Analysis System with Feature Selection and Classifier Ensemble
Efficient Twitter Sentiment Analysis System with Feature Selection and Classifier Ensemble
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DOI：
10.1007/978-3-319-74690-6_51
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
M. Fouad;Tarek F. Gharib;A. Mashat
中科院分区：
文献类型：
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作者：
M. Fouad;Tarek F. Gharib;A. Mashat
Twitter的情感分析是近年来研究的热点之一。它将自然语言处理技术与数据挖掘方法相结合，用于构建此类系统。在本文中，我们介绍了一个有效的Twitter情感分析系统。该系统建立了一个机器学习模型，用于检测积极和消极的推文。该模型使用不同的技术来表示训练阶段使用不同特征集的输入标记推文。在分类阶段，分类器集成与不同的基分类器，更准确的结果。所提出的系统可以用于测量用户的意见，从他们的鸣叫，这是非常有用的，在许多应用中，如营销，政治极性检测和审查产品。
Sentiment analysis from Twitter is one of the interesting research fields recently. It combines natural language processing techniques with the data mining approaches for building such systems. In this paper, we introduced an efficient system for Twitter sentiment analysis. The proposed system built a machine learning model for detecting positive and negative tweets. This model used different techniques to represent the input labeled tweets in the training phase using different features sets. In the classification phase, the classifier ensemble is presented with different base classifiers for more accurate results. The proposed system can be used for measuring users’ opinion from their tweets which is very useful in many applications such as marketing, political polarity detection and reviewing products.