• Jun 03, 2019 News!Vol.9, No.5-Vol.10, No.3 have been indexed by EI (Inspec).   [Click]
  • Jun 18, 2021 News!Vol.13, No.3 has been published with online version.   [Click]
  • Apr 09, 2021 News!Vol.13, No.2 has been published with online version.   [Click]
General Information
Editor-in-chief
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2020 Vol.12(6): 145-150 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2020.V12.1280

Building a Libyan Dialect Lexicon-Based Sentiment Analysis System Using Semantic Orientation of Adjective-Adverb Combinations

Husien A. Alhammi and Kais Haddar
Abstract—Twitter is a social media network website, where its users can post their opinions and sentiments about issues, objects, services, places or people in short text messages called tweets. The sentiment information which is extracted from analyzing tweets is very useful in various aspects such as business, governments and so on. Although Arabic dialects social media sentiment analysis has attracted several studies, yet there has been almost no work on the Libyan dialect sentiment analysis. In this research, an adjective priority scoring algorithm which calculates the sentiment orientation of adjective-adverb combinations is used to build a fine-grained sentiment analysis system for classifying Libyan dialect tweets into seven categories. Therefore, we exploit a freely available Libyan dialect twitter corpus, which contains 5000 sentences or tweets to carry out our work, the tweets in the corpus were equally divided into two data sets (study and test). Adjectives and adverbs in the study data set were manually collected to construct sentiment dictionaries or lexicons. Consequently, approximately 108 adjectives were stored in a adjectives dictionary, the polarities or semantic orientation scores of these adjectives were manually assigned by two annotators in the range of [+2,-2]. Likewise, each adverb of degree was scored in the range from 0 to 1 and stored them in a separate dictionary which totally contains 27 adverbs. Our system yields an F-score of 82.19% on the test data set.

Index Terms—Sentiment analysis, semantic orientation, tweets sentiment analysis, adverbs of degree, adverb-adjective combinations, Twitter Libyan dialect.

Husien A. Alhammi is with the Department of Electrical and Electronic, Higher Institute of Science and Technology of Zawia, Libya (e-mail: h1974hami@gmail.com). Kais Haddar is with Multimedia Information Systems and Advanced Computing Laboratory University of Sfax, Tunisia (e-mail: Kais.Haddar@fss.rnu.tn).

[PDF]

Cite:Husien A. Alhammi and Kais Haddar, "Building a Libyan Dialect Lexicon-Based Sentiment Analysis System Using Semantic Orientation of Adjective-Adverb Combinations," International Journal of Computer Theory and Engineering vol. 12, no. 6, pp. 145-150, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


Copyright © 2008-2021. International Association of Computer Science and Information Technology. All rights reserved.