Hybrid Ensemble Learning With Feature Selection for Sentiment Classification in Social Media

Sanur Sharma and Anurag Jain
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Sanur Sharma: Guru Gobind Singh Indraprastha University, Delhi, India
Anurag Jain: Guru Gobind Singh Indraprastha University, Delhi, India

International Journal of Information Retrieval Research (IJIRR), 2020, vol. 10, issue 2, 40-58

Abstract: This article presents a study on ensemble learning and an empirical evaluation of various ensemble classifiers and ensemble features for sentiment classification of social media data. The data was collected from Twitter in real-time using Twitter API and text pre-processing and ranking-based feature selection is applied to textual data. A framework for a hybrid ensemble learning model is presented where a combination of ensemble features (Information Gain and CHI-Squared) and ensemble classifier that includes Ada Boost with SMO-SVM and Logistic Regression has been implemented. The classification of Twitter data is performed where sentiment analysis is used as a feature. The proposed model has shown improvements as compared to the state-of-the-art methods with an accuracy of 88.2% with a low error rate.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jirr00:v:10:y:2020:i:2:p:40-58

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