Imbalanced Sentiment Polarity Detection Using Emoji-Based Features and Bagging Ensemble
Sadam Al-Azani, El-Sayed M. El-Alfy · 2018
The research on the sentiment analysis of social media content is remarkably growing for constructing resources and investigating new ideas and techniques to address various challenges. This paper explores a new approach for sentiment polarity detection in Arabic text using non-verbal emoji-based features while addressing the class imbalance problem. The proposed method is based on Bootstrap Aggregating (Bag-ging) algorithm and Synthetic Minority Oversampling Technique (SMOTE) to build and combine multiple models from the training dataset. Three different classifiers are evaluated as single and ensemble classifiers: naive Bayes, k-NN and decision trees. The performance is evaluated and compared on three datasets with a varying imbalance ratio ranging from two to more than seven. The experimental results show that the proposed approach performs better than other approaches in most of the considered cases.