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, researchers 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 scores (60%) Positive and (40%) Negative. The general accuracy of the lexicons was (98%). The lexicon was used to label a set of unlabelled Facebook comments to formulate a big dataset. Using supervised Machine Learning (ML) algorithms that are usually used in polarity classification, researchers introduce 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 (KNN), and Naive Bayes (NB) Classifiers respectively
Key words: Jordan Telecom, Sentiment Analysis, Lexicon-Based, Polarity, Facebook Comments, Machine Learning, NLP
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