Subjective Text Mining for Arabic Social Media

Subjective Text Mining for Arabic Social Media

Nourah F. Bin Hathlian (College of Arts and Sciences, Nairiyah University of Hafer Albatin, Alkhbar, Saudi Arabia) and Alaaeldin M. Hafez (College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia)
Copyright: © 2017 |Pages: 13
DOI: 10.4018/IJSWIS.2017040101
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The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.
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Sentiment analysis has evolved as a unique way of text analytics because of the increase of new opinionated data in social media. Sentiment analysis is divided into two main processes: the collection of data sets (corpus) and the categorization of the data depending on their sentiments. In fact, sentiment analysis entails the detection of opinions within the text and the distinction between their polarity classes, whether positive or negative.

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