An Ensemble of Arabic Transformer-based Models for Arabic Sentiment Analysis
Ikram El Karfi, Sanaa El Fkihi
Abstract
In recent years, sentiment analysis has gained momentum as a research area. This task aims at identifying the opinion that is expressed in a subjective statement. An opinion is a subjective expression describing personal thoughts and feelings. These thoughts and feelings can be assigned with a certain sentiment. The most studied sentiments are positive, negative, and neutral. Since the introduction of attention mechanism in machine learning, sentiment analysis techniques have evolved from recurrent neural networks to transformer models. Transformer-based models are encoder-decoder systems with attention. Attention mechanism has permitted models to consider only relevant parts of a given sequence. Making use of this feature in encoder-decoder architecture has impacted the performance of transformer models in several natural language processing tasks, including sentiment analysis. A significant number of Arabic transformer-based models have been pre-trained recently to perform Arabic sentiment analysis tasks. Most of these models are implemented based on Bidirectional Encoder Representations from Transformers (BERT) such as AraBERT, CAMeLBERT, Arabic ALBERT and GigaBERT. Recent studies have confirmed the effectiveness of this type of models in Arabic sentiment analysis. Thus, in this work, two transformer-based models, namely AraBERT and CAMeLBERT have been experimented. Furthermore, an ensemble model has been implemented to achieve more reasonable performance.