Sentiment analysis and classification of Arab Jordanian Facebook comments for Jordanian telecom companies using lexicon-based approach and machine learning
المؤلفون المشاركون
Nahar, Khalid Muhammad Uqlah
Jaradat, Amirah S.
Atum, Muhammad Salim
Ibrahim, Firas
المصدر
Jordanian Journal of Computetrs and Information Technology
العدد
المجلد 6، العدد 3 (30 سبتمبر/أيلول 2020)، ص ص. 247-262، 16ص.
الناشر
جامعة الأميرة سمية للتكنولوجيا
تاريخ النشر
2020-09-30
دولة النشر
الأردن
عدد الصفحات
16
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Nahar, Khalid Muhammad Uqlah& Jaradat, Amirah S.& Atum, Muhammad Salim& Ibrahim, Firas. 2020. Sentiment analysis and classification of Arab Jordanian Facebook comments for Jordanian telecom companies using lexicon-based approach and machine learning. Jordanian Journal of Computetrs and Information Technology،Vol. 6, no. 3, pp.247-262.
https://search.emarefa.net/detail/BIM-1415642
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Nahar, Khalid Muhammad Uqlah…[et al.]. Sentiment analysis and classification of Arab Jordanian Facebook comments for Jordanian telecom companies using lexicon-based approach and machine learning. Jordanian Journal of Computetrs and Information Technology Vol. 6, no. 3 (Sep. 2020), pp.247-262.
https://search.emarefa.net/detail/BIM-1415642
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Nahar, Khalid Muhammad Uqlah& Jaradat, Amirah S.& Atum, Muhammad Salim& Ibrahim, Firas. Sentiment analysis and classification of Arab Jordanian Facebook comments for Jordanian telecom companies using lexicon-based approach and machine learning. Jordanian Journal of Computetrs and Information Technology. 2020. Vol. 6, no. 3, pp.247-262.
https://search.emarefa.net/detail/BIM-1415642
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 260-261
رقم السجل
BIM-1415642
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر