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Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis
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Author(s): Ishrat Nazeer (School of Computer Science and Engineering, Lovely Professional University, Jalandhar, India), Mamoon Rashid (School of Computer Science and Engineering, Lovely Professional University, Jalandhar, India), Sachin Kumar Gupta (School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Jammu, India)and Abhishek Kumar (School of Computer Science and IT, Jain University, Bangalore, India)
Copyright: 2021
Pages: 13
Source title:
Analyzing Global Social Media Consumption
Source Author(s)/Editor(s): Patrick Kanyi Wamuyu (United States International University – Africa, Kenya)
DOI: 10.4018/978-1-7998-4718-2.ch002
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Abstract
Twitter is a platform where people express their opinions and come with regular updates. At present, it has become a source for many organizations where data will be extracted and then later analyzed for sentiments. Many machine learning algorithms are available for twitter sentiment analysis which are used for automatically predicting the sentiment of tweets. However, there are challenges that hinder machine learning classifiers to achieve better results in terms of classification. In this chapter, the authors are proposing a novel feature generation technique to provide desired features for training model. Next, the novel ensemble classification system is proposed for identifying sentiment in tweets through weighted majority rule ensemble classifier, which utilizes several commonly used statistical models like naive Bayes, random forest, logistic regression, which are weighted according to their performance on historical data, where weights are chosen separately for each model.
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