A Classifier Ensemble Approach to Detect Emotions Polarity in Social Media
Abstract: The advent of social media has changed completely the role of the users and has transformed them from simple passive information seekers to active producers. The user generated textual data in social media and microblogging platforms are rich in emotions, opinions and attitudes and necessitate automated methods to analyse and extract knowledge from them. In this paper, we present a classifier ensemble approach to detect emotional content in social media and examine its performance under bagging and boosting combination methods. The classifier ensemble aims to take advantage of the base classifiers’ benefits and constitutes a promising approach to detect sentiments in social media. Our classifier ensemble combines a knowledge based tool that performs deep analysis of the natural language and two machine learning classifiers, a Naïve Bayes and a Maximum Entropy which are trained on ISEAR and Affective text datasets. The evaluation study conducted revealed quite promising results and in
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