Sentiment analysis and classification of arab jordanian facebook comments for jordanian telecom companies using lexicon-based approach and machine learning

Abstract

Sentiment Analysis (SA) is a technique used for identifying the polarity (positive, negative) of a given text, using Natural Language Processing (NLP) techniques. Facebook is an example of a social media platform that is widely used among people living in Jordan to express their opinions regarding public and special focus areas. In this paper, we implemented the lexicon-based approach for identifying the polarity of the provided Facebook comments. The data samples are from local Jordanian people commenting on a public issue related to the services provided by the main telecommunication companies in Jordan (Zain, Orange and Umniah). The produced results regarding the evaluated Arabic sentiment lexicon were promising. By applying the user-defined lexicon based on the common Facebook posts and comments used by Jordanians, it scored (60%) positive and (40%) negative. The general lexicon accuracy was (98%). The lexicon was used to label a set of unlabeled Facebook comments to formulate a big dataset. Using supervised Machine Learning (ML) algorithms that are usually used in polarity classification, the researchers introduced them to our formulated dataset. The results of the classification were 97.8, 96.8 and 95.6% for Support Vector Machine (SVM), K-Nearest Neighbour (K-NN) and Naïve Bayes (NB) classifiers, respectively.