An efficient approach for improving customer Sentiment Analysis in the Arabic language using an Ensemble machine learning technique

The complexity of the Arabic language in terms of morphology, orthography, and dialects renders it more difficult to conduct sentiment analysis for the Arabic language. This issue is made much more challenging by the practice of extracting text features from short communications to evaluate the tone of the communication. On the other hand, the technique for analyzing and assessing sentiment faces a great deal of difficulty. These issues might be hampered by the accurate interpretation of sentiments and identifying the appropriate polarity of sentiment. Sentiment analysis can recognize and extract subjective information from the text. This study intends to investigate the effectiveness of various Machine Learning (ML) techniques in understanding the sentiments conveyed by the Arabic language. In addition, the feature extraction from the dataset was carried out with the help of the Term Frequency-Inverse Document Frequency (TF-IDF). As a consequence of this, the techniques of Adaboost classifier (AC), Support Vector Machine (SVM), Maximum Entropy (ME), Decision tree (DT), and K-Nearest Neighbors (KNN) are utilized in the process of sentiment analysis (SA). In conclusion, a model for ensemble-based sentiment analysis was developed. Compared to other machine learning classifiers cited earlier, we achieved better performance in terms of accuracy, precision, kappa, and ROC AUC-score for ensemble classifiers with 10-fold cross-validation.