IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis

Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis
View Sample PDF
Author(s): Nawaf A. Abdulla (Jordan University of Science and Technology, Jordan), Nizar A. Ahmed (Jordan University of Science and Technology, Jordan), Mohammed A. Shehab (Jordan University of Science and Technology, Jordan), Mahmoud Al-Ayyoub (Jordan University of Science and Technology, Jordan), Mohammed N. Al-Kabi (Zarqa University, Jordan)and Saleh Al-rifai (Jordan University of Science and Technology, Jordan)
Copyright: 2016
Pages: 17
Source title: Big Data: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-9840-6.ch091

Purchase

View Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis on the publisher's website for pricing and purchasing information.

Abstract

The emergence of the Web 2.0 technology generated a massive amount of raw data by enabling Internet users to post their opinions on the web. Processing this raw data to extract useful information can be a very challenging task. An example of important information that can be automatically extracted from the users' posts is their opinions on different issues. This problem of Sentiment Analysis (SA) has been studied well on the English language and two main approaches have been devised: corpus-based and lexicon-based. This work focuses on the later approach due to its various challenges and high potential. The discussions in this paper take the reader through the detailed steps of building the main two components of the lexicon-based SA approach: the lexicon and the SA tool. The experiments show that significant efforts are still needed to reach a satisfactory level of accuracy for the lexicon-based Arabic SA. Nonetheless, they do provide an interesting guide for the researchers in their on-going efforts to improve lexicon-based SA.

Related Content

. © 2023. 34 pages.
. © 2023. 15 pages.
. © 2023. 15 pages.
. © 2023. 18 pages.
. © 2023. 24 pages.
. © 2023. 32 pages.
. © 2023. 21 pages.
Body Bottom