Arabic Sentiment Analysis Evaluation of Saudi Arabia's Telecommunication Using Several Deep Learning Algorithms

Abstract

Sentiment analysis (SA) is a technique that applies natural language processing (NLP) in order to analyze and classify the emotion in sentiment reviews. SA is responsible for analyzing people's feelings, opinions, and experiences that are shared through the Internet and social networks. In this paper, we focus on investigating, evaluating, and improving Arabic sentiment analysis (ASA) models, datasets, and challenges. ASA has several difficulties, like language’s morphological features, many dialects, no clear and uniform corpora, low accuracy, and restricted ASA material. In order to do that, we do a full analysis and evaluation of Arabic sentiment analysis models and datasets that target e-marketing services such as telecommunication, health, and books. We evaluate our data set, called Sara, with several Arabic sentiment datasets in terms of brief description, dataset size, source of collecting data, field type, and abbreviation. We enhanced our previous models by using ensemble learning average techniques. The accuracy of our enhanced model has increased and now reaches around 97%. Also, we evaluate our developed ASA using deep learning (DL) algorithms with other ASA models in the field of e-marketing. Our models have significant improvements in terms of performance compared with other works, where our three models, CNN-Model, LSTM-Mode2, and CNN+LSTM-Model3, have accuracy of 96.83%, 94.74%, and 96.91%, respectively.