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MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
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In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
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Abstract:
The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialects.
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Keyword:
Arabic NLP; Arabic social media; lexical; Saudi dialects; sentiment analysis; Twitter
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URL: https://doi.org/10.3390/app12083806
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| 2 |
Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian
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In: Electronics; Volume 11; Issue 3; Pages: 374 (2022)
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| 3 |
A New Ontology-Based Method for Arabic Sentiment Analysis
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In: Big Data and Cognitive Computing; Volume 6; Issue 2; Pages: 48 (2022)
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| 4 |
COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset
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In: Healthcare; Volume 10; Issue 3; Pages: 411 (2022)
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| 5 |
Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model
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In: Applied Sciences; Volume 12; Issue 2; Pages: 692 (2022)
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| 6 |
Analysis of Destination Images in the Emerging Ski Market: The Case Study in the Host City of the 2022 Beijing Winter Olympic Games
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In: Sustainability; Volume 14; Issue 1; Pages: 555 (2022)
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| 7 |
Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2707 (2022)
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| 8 |
Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
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In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
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| 9 |
How We Failed in Context: A Text-Mining Approach to Understanding Hotel Service Failures
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In: Sustainability; Volume 14; Issue 5; Pages: 2675 (2022)
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| 10 |
Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
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| 11 |
How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 3; Pages: 1822 (2022)
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| 12 |
TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels
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In: Data; Volume 7; Issue 1; Pages: 8 (2022)
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| 13 |
Detecting Depression Signs on Social Media: A Systematic Literature Review
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In: Healthcare; Volume 10; Issue 2; Pages: 291 (2022)
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| 18 |
Using Campaign Communications to Analyze Civility in Ranked Choice Voting Elections
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In: Politics and Governance ; 9 ; 2 ; 280-292 ; The Politics, Promise and Peril of Ranked Choice Voting (2022)
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| 19 |
Tracing the Legitimacy of Artificial Intelligence – A Media Analysis, 1980-2020
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| 20 |
Understanding cyberslacking intention during Covid-19 online classes: An fsQCA analysis
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