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Rahat Iqbal

Rahat Iqbal has 3 papers in our library, in 2017–2018, most of them on Sentiment & emotion, Semitic languages; often with Abdulaziz M. Alayba, Matthew England, Vasile Palade.

3 papers in our library · 2017–2018

OpenAlex · ORCID · h-index 25 · 149 works · 2,545 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 26 times in our library · ⭐ Paper Stars ranking: #4353

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢CoventryCoventry · United Kingdom2003–2026 · 10 paperscurrent🎓Coventry UniversityCoventry · United Kingdom2003–2022 · 119 papers🎓University of DubaiDubai · United Arab Emirates2021–2023 · 9 papers🎓Eindhoven University of TechnologyEindhoven · Netherlands2005–2006 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksComputer Science · 3 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksExperimental and Cognitive Psychology · 1 workPsychology · 1 work

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 3 worksSentiment & emotion · 3 works

 Works by year

2010s

20182 papers20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Combined CNN and LSTM Model for Arabic Sentiment Analysis (2018 · 217 citations)
    The paper tackles binary (positive/negative) sentiment classification of Arabic tweets. This is hard because tweets are short, Arabic morphology is rich (a single verb takes many gendered, numbered and tensed forms), and reliable preprocessing tools are scarce, especially for dialectal text. The authors stack a CNN…
  2. Arabic Language Sentiment Analysis on Health Services (2017 · 126 citations)
    The work tackles the shortage of Arabic sentiment resources by building a Twitter corpus of opinions about health services.
  3. Improving Sentiment Analysis in Arabic Using Word Representation (2018 · 59 citations)
    Sentiment classification for Arabic is difficult: the language has rich morphology, inconsistent spelling (the Hamza letter is a frequent source of errors) and many dialects, and tweets are very short.

 Co-authors here

3 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 26 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated

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