Sentiment analysis resource of Libyan dialect for Libyan Airlines

Abstract

Arabic lacks extensive corpora for natural language processing (NLP) when compared to other languages, namely in the Libyan dialect (LD). Therefore, this study proposes the first corpus of Arabic sentiment analysis (ASA) of the Libyan Dialect for the Airline Industry (ASALDA). It comprises 9,350 comments and tweets, annotating them manually depending on text polarity into three labels: positive, negative, and neutral, and utilized aspect-based sentiment analysis (SA) to annotate opinions regarding fifteen aspects. Also constructs a simple sentiment lexicon of the LD. The solution is based on the idea that the corpus and lexicon can be helpful models to improve classification for the LD. The approach has notable merits, namely creating a corpus and sentiment lexicon for the LD from comments and tweets of airline companies. A comprehensive verification using a statistical technique called the chi-square test is carried out with the corpus to determine if two aspects are related to one another. Based on the statistical work, we found that airlines should focus on improving their services in aspects where they are performing poorly, such as late flights, customer service, or price. The corpus and lexicon that we proposed can be utilized to perform many opinion mining and SA experimentations using machine learning and deep learning.