Sentiment Analysis from Texts Written in Standard Arabic and Moroccan Dialect based on Deep Learning Approaches.

Abstract

Sentiment analysis plays a crucial role in extracting subjective information from various sources using natural language processing techniques. It involves identifying opinions, attitudes, and emotions towards specific topics or documents. This study focuses on evaluating the performance of machine learning, deep learning, and transfer learning algorithms in accurately classifying positive and negative sentiments in Arabic comments. The study uses different machine learning and deep learning techniques, including the use of Arabert. A transfer learning technique based on the BERT algorithm for Arabic language processing. Arabert is pre-trained on a vast corpus of Arabic data, which allows him to capture complex Arabic-specific linguistic patterns. It is then refined onto a smaller dataset of Arabic comments for sentiment analysis. The study will outline the important steps and processes involved in each approach, highlighting their strengths, and comparing their performance. The utilization of deep learning and transfer learning techniques, such as Arabert, has the potential to enhance sentiment analysis accuracy on Arabic comments. By comparing the performance of different methods, the study aims to identify the most effective approaches for sentiment analysis in Arabic text. The findings of this research have practical implications in improving sentiment analysis accuracy for Arabic language applications, particularly when working with limited labeled datasets. The results can be valuable in fields like market research, customer service, and social media analysis, providing insights into the attitudes, opinions, and emotions expressed by Arabic-speaking users.