SentiXGboost: enhanced sentiment analysis in social media posts with ensemble XGBoost classifier

Abstract

Sentiment Analysis has emerged as one of the most challenging and significant research directions in text mining. The first milestone of Sentiment Analysis involves identifying the polarity of sentiments in social media and classifying them as positive or negative. Most of the previous work on sentiment analysis has focused on designing a classifier, for a specific dataset, using supervised machine learning approaches and feature extraction methods. Notably, choosing the most appropriate classification technique to correctly classify the sentiment polarity of text is crucial. Thus, this study proposes a novel ensemble classifier approach, which utilizes a combination of multiple feature sets with ensemble classification by combining multiple base classifiers, which are weak learners, into an ensemble classifier. These feature sets include Bag of Words, Term Frequency-Inverse Document Frequency, Part of Speech, N-gram, Opinion Lexicon, and Term Frequency. The experiments confirm that the proposed ensemble method outperforms all individual classifiers and significantly improves the overall sentiment classification performance on the most frequently used datasets in Sentiment Analysis.