Towards Analyzing the Sentiments in the Fields of Automobiles and Real-Estates with Specific Focus on Arabic Online Reviews

Abstract

The importance of performing sentiment analysis is noticed in various fields, such as politics, education, marketing, and so forth. However, in the Arabic language domain, there are limited studies that focus on analyzing the sentiments in the text in comparison to the English language. There is a lack of available annotated Arabic datasets covering specific domains (such as real-estates and automobiles) and containing data written in both modern standard Arabic (MSA) and the Gulf Cooperation Council (GCC) dialect. Furthermore, the limited and inadequate adoption of natural language processing and machine learning techniques is noteworthy in the current sentiment analysis contributions targeting the Arabic language. Therefore, the gap could be bridged by creating real-estates and automobiles datasets. Moreover, customizing, enhancing, and applying suitable natural language processing techniques and machine learning algorithms to analyze the sentiments in these datasets will also contribute to filling the current gap. Performing these steps will benefit the people interested in analyzing the sentiments related to real-estates and automobiles, and will add a new scope to the Arabic sentiment analysis field. Future researchers in this field could also be benefited by using the datasets that will be freely available. The aforementioned factors encouraged the researcher to conduct this research in order to fill the current gap in this area.