Reference Hub

This research has been cited in:

Article
AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text CorpusApplied Sciences10.3390/app11052434
Conference
Building a standard dataset for Arabie sentiment analysis: Identifying potential annotation pitfalls2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)10.1109/AICCSA.2016.7945822
Conference
Arabic sentiment analysis of YouTube comments2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)10.1109/AEECT.2017.8257766
Chapter
Dataset Built for Arabic Sentiment AnalysisProceedings of the International Conference on Advanced Intelligent Systems and Informatics 201710.1007/978-3-319-64861-3_38
Article
Sentiment Analysis of Users on Social Networks: Overcoming the challenge of the Loose Usages of the Algerian DialectProcedia Computer Science10.1016/j.procs.2018.10.458
Article
A Survey of Opinion Mining in ArabicACM Transactions on Asian and Low-Resource Language Information Processing10.1145/3295662
Chapter
Towards Improving Sentiment Analysis in ArabicProceedings of the International Conference on Advanced Intelligent Systems and Informatics 201610.1007/978-3-319-48308-5_12
Article
Sentiment Analysis on Urdu Tweets Using Markov ChainsSN Computer Science10.1007/s42979-020-00279-9
Article
Recent developments in information extraction approaches from Arabic tweets on social networking sitesInternational Journal of ADVANCED AND APPLIED SCIENCES10.21833/ijaas.2022.09.018
Article
Successes and challenges of Arabic sentiment analysis research: a literature reviewSocial Network Analysis and Mining10.1007/s13278-017-0474-x
Chapter
Evaluation and Enrichment of Arabic Sentiment AnalysisIntelligent Natural Language Processing: Trends and Applications10.1007/978-3-319-67056-0_2
Article
A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian DialectSN Computer Science10.1007/s42979-021-00510-1
Article
Hybrid Algorithm to Evaluate E-Business Website CommentsCommunications and Network10.4236/cn.2016.83014
Conference
Sentiment classification techniques for Arabic language: A survey2016 7th International Conference on Information and Communication Systems (ICICS)10.1109/IACS.2016.7476075
Article
An application of MOGW optimization for feature selection in text classificationThe Journal of Supercomputing10.1007/s11227-020-03490-w
Conference
Multi-way sentiment classification of Arabic reviews2015 6th International Conference on Information and Communication Systems (ICICS)10.1109/IACS.2015.7103228
Chapter
MYC: A Moroccan Corpus for Sentiment AnalysisAdvances in Machine Intelligence and Computer Science Applications10.1007/978-3-031-29313-9_6
Conference
Exploring Differences in the Impact of Users’ Traces on Arabic and English Facebook SearchIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352522
Conference
Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)10.1109/AEECT.2015.7360595
Conference
Building a Sentiment Analysis system using automatically generated training DatasetProceedings of the 2019 8th International Conference on Software and Information Engineering10.1145/3328833.3328874
Conference
Emotion analysis of Arabic articles and its impact on identifying the author's gender2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA)10.1109/AICCSA.2015.7507196
Conference
Sentiment Analysis Based Fuzzy Decision Platform for the Saudi Stock Market2018 IEEE International Conference on Electro/Information Technology (EIT)10.1109/EIT.2018.8500292
Article
A set of parameters for automatically annotating a Sentiment Arabic CorpusInternational Journal of Web Information Systems10.1108/IJWIS-03-2019-0008
Chapter
Text Mining for Word Sentiment DetectionKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-319-99701-8_7
Article
A comprehensive survey of arabic sentiment analysisInformation Processing & Management10.1016/j.ipm.2018.07.006
Article
ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messagesSocial Network Analysis and Mining10.1007/s13278-020-00688-x
Article
KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment AnalysisNew Generation Computing10.1007/s00354-022-00182-2
Chapter
Towards a New Lexicon-Based Features Vector for Sentiment Analysis: Application to Moroccan Arabic TweetsAdvances in Information, Communication and Cybersecurity10.1007/978-3-030-91738-8_7
Article
Predicting Violence-Induced Stress in an Arabic Social Media ForumIntelligent Automation & Soft Computing10.32604/iasc.2023.028067
Article
A systematic review of social network sentiment analysis with comparative study of ensemble-based techniquesArtificial Intelligence Review10.1007/s10462-023-10472-w
Article
The Prediction of Consumer Behavior from Social Media ActivitiesBehavioral Sciences10.3390/bs12080284
Article
Studying the effect of characteristic vector alteration on Arabic sentiment classificationJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2019.04.011
Article
Predicting Depression Symptoms in an Arabic Psychological ForumIEEE Access10.1109/ACCESS.2020.2981834
Article
Sentiment analysis for mining texts and social networks data: Methods and toolsWIREs Data Mining and Knowledge Discovery10.1002/widm.1333
Article
Arabic sentiment analysis: studies, resources, and toolsSocial Network Analysis and Mining10.1007/s13278-019-0602-x
Conference
Performance Comparison of Different Lexicons for Sentiment Analysis in Arabic2016 Third European Network Intelligence Conference (ENIC)10.1109/ENIC.2016.026
Article
Deep analysis of an Arabic sentiment classification system based on lexical resource expansion and custom approaches buildingInternational Journal of Speech Technology10.1007/s10772-020-09758-z
Conference
Lexicon Addition Effect on Lexicon-Based of Indonesian Sentiment Analysis on Twitter2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)10.1109/ICIMCIS51567.2020.9354269
Chapter
Sentiment Analysis in ArabicNatural Language Processing and Information Systems10.1007/978-3-319-41754-7_41
Article
AraCust: a Saudi Telecom Tweets corpus for sentiment analysisPeerJ Computer Science10.7717/peerj-cs.510
Article
A review of sentiment analysis research in Arabic languageFuture Generation Computer Systems10.1016/j.future.2020.05.034
Chapter
Analyzing User Behavior and Sentimental in Computer Mediated CommunicationImage Processing and Capsule Networks10.1007/978-3-030-51859-2_26
Article
A lexicon-based approach for sentiment analysis of multimodal content in tweetsMultimedia Tools and Applications10.1007/s11042-023-14411-3
Article
Classification of Arabic Tweets: A ReviewElectronics10.3390/electronics10101143
Article
Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysisJournal of Information Science10.1177/0165551516688143
Chapter
Construction and Exploitation of an Algerian Corpus for Opinion and Emotion AnalysisAdvances in Knowledge Discovery and Management10.1007/978-3-030-90287-2_1
Conference
Evaluating SentiStrength for Arabic Sentiment Analysis2016 7th International Conference on Computer Science and Information Technology (CSIT)10.1109/CSIT.2016.7549458
Article
Arabic Sentiment Analysis for Twitter Data: A Systematic Literature ReviewEngineering, Technology & Applied Science Research10.48084/etasr.5662
Chapter
Sentiment Analysis of Arabic Tweets for Road Traffic Congestion and Event DetectionSmart Infrastructure and Applications10.1007/978-3-030-13705-2_2
Article
The Happy Level: A New Approach to Measure Happiness at Work Using Mixed MethodsInternational Journal of Qualitative Methods10.1177/16094069211002413
Article
Iktishaf: a Big Data Road-Traffic Event Detection Tool Using Twitter and Spark Machine LearningMobile Networks and Applications10.1007/s11036-020-01635-y
Chapter
MAC: An Open and Free Moroccan Arabic Corpus for Sentiment AnalysisInnovations in Smart Cities Applications Volume 510.1007/978-3-030-94191-8_68
Conference
Opinion and Sentiment Polarity Detection Using Supervised Machine Learning2018 IEEE 5th International Congress on Information Science and Technology (CiSt)10.1109/CIST.2018.8596563
Chapter
Arabic Sentiment Analysis Resources: A SurveySocial Computing and Social Media10.1007/978-3-319-39910-2_25
Article
Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social mediaJournal of Big Data10.1186/s40537-021-00488-w
Article
Sentiment lexicon for sentiment analysis of Saudi dialect tweetsProcedia Computer Science10.1016/j.procs.2018.10.494
Article
Using Enhanced Lexicon-Based Approaches for the Determination of Aspect Categories and Their Polarities in Arabic ReviewsInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.2016070102
Article
Polarity Classification of Arabic SentimentsInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.2016070103
Chapter
Artificial Intelligence and Machine Learning-Based Advanced Depression Detection SystemHandbook of Research on Applied Intelligence for Health and Clinical Informatics10.4018/978-1-7998-7709-7.ch006
Chapter
Sentiment Analysis of Social Media as Tool to Improve Customer RetentionStrategic Uses of Social Media for Improved Customer Retention10.4018/978-1-5225-1686-6.ch011
Chapter
Sentiment Analysis of Social Media as Tool to Improve Customer RetentionDigital Marketing and Consumer Engagement10.4018/978-1-5225-5187-4.ch032
Article
Real-Time Unspecified Major Sub-Events Detection in the Twitter Data Stream That Cause the Change in the Sentiment Score of the Targeted EventInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.2017100101

Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis

Nawaf A. Abdulla, Nizar A. Ahmed, Mohammed A. Shehab, Mahmoud Al-Ayyoub, Mohammed N. Al-Kabi, Saleh Al-rifai

Source Title: International Journal of Information Technology and Web Engineering (IJITWE)9(3)

ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781466655317|DOI: 10.4018/ijitwe.2014070104

Cite Article Cite Article

MLA

Abdulla, Nawaf A., et al. "Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis." IJITWE vol.9, no.3 2014: pp.55-71. http://doi.org/10.4018/ijitwe.2014070104

APA

Abdulla, N. A., Ahmed, N. A., Shehab, M. A., Al-Ayyoub, M., Al-Kabi, M. N., & Al-rifai, S. (2014). Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis. International Journal of Information Technology and Web Engineering (IJITWE), 9(3), 55-71. http://doi.org/10.4018/ijitwe.2014070104

Chicago

Abdulla, Nawaf A., et al. "Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis," International Journal of Information Technology and Web Engineering (IJITWE) 9, no.3: 55-71. http://doi.org/10.4018/ijitwe.2014070104

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

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.

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis

MLA

APA

Chicago

Export Reference

Abstract

Request Access