Iqra Ameer

419 total citations
19 papers, 192 citations indexed

About

Iqra Ameer is a scholar working on Artificial Intelligence, Information Systems and Social Psychology. According to data from OpenAlex, Iqra Ameer has authored 19 papers receiving a total of 192 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 5 papers in Information Systems and 4 papers in Social Psychology. Recurrent topics in Iqra Ameer's work include Sentiment Analysis and Opinion Mining (9 papers), Spam and Phishing Detection (5 papers) and Text and Document Classification Technologies (4 papers). Iqra Ameer is often cited by papers focused on Sentiment Analysis and Opinion Mining (9 papers), Spam and Phishing Detection (5 papers) and Text and Document Classification Technologies (4 papers). Iqra Ameer collaborates with scholars based in Mexico, United States and Pakistan. Iqra Ameer's co-authors include Grigori Sidorov, Alexander Gelbukh, Rao Muhammad Adeel Nawab, Burcu Can, Helena Gómez-Adorno, Noman Ashraf, Muhammad Ijaz, Muhammad Zahid, Irfan Ullah and Zhengbing He and has published in prestigious journals such as Scientific Reports, Expert Systems with Applications and IEEE Access.

In The Last Decade

Iqra Ameer

17 papers receiving 189 citations

Peers

Iqra Ameer
Iqra Ameer
Citations per year, relative to Iqra Ameer Iqra Ameer (= 1×) peers Qianyao Xu

Countries citing papers authored by Iqra Ameer

Since Specialization
Citations

This map shows the geographic impact of Iqra Ameer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Iqra Ameer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Iqra Ameer more than expected).

Fields of papers citing papers by Iqra Ameer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Iqra Ameer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Iqra Ameer. The network helps show where Iqra Ameer may publish in the future.

Co-authorship network of co-authors of Iqra Ameer

This figure shows the co-authorship network connecting the top 25 collaborators of Iqra Ameer. A scholar is included among the top collaborators of Iqra Ameer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Iqra Ameer. Iqra Ameer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Torres-Ruiz, Miguel, et al.. (2025). An Efficient Approach for Code-Mixed Emotion Classification Applying Machine Learning. IEEE Access. 13. 166973–166986.
2.
Ameer, Iqra, et al.. (2025). Multilingual hope speech detection from tweets using transfer learning models. Scientific Reports. 15(1). 9005–9005. 2 indexed citations
3.
Sidorov, Grigori, et al.. (2025). Opioid Crisis Detection in Social Media Discourse Using Deep Learning Approach. Information. 16(7). 545–545. 2 indexed citations
4.
Ahmad, Muhammad, Hany Farid, Iqra Ameer, et al.. (2025). UE-NER-2025: A GPT-Based Approach to Multi-Lingual Named Entity Recognition on Urdu and English. IEEE Access. 13. 111175–111186. 2 indexed citations
6.
Farid, Hany, et al.. (2024). Hope Speech Detection Using Social Media Discourse (Posi-Vox-2024): A Transfer Learning Approach. Journal of language and Education. 10(4). 31–43. 2 indexed citations
7.
Ameer, Iqra, et al.. (2024). Analyzing hope speech from psycholinguistic and emotional perspectives. Scientific Reports. 14(1). 23548–23548. 2 indexed citations
8.
Ameer, Iqra, et al.. (2024). Mental Illness Classification on Social Media Texts Using Deep Learning and Transfer Learning. Computación y Sistemas. 28(2). 3 indexed citations
9.
Zahid, Muhammad, et al.. (2024). Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques. Traffic Injury Prevention. 25(3). 472–481. 25 indexed citations
10.
Ameer, Iqra, et al.. (2023). Emotion Classification in Texts Over Graph Neural Networks: Semantic Representation is Better Than Syntactic. IEEE Access. 11. 56921–56934. 9 indexed citations
11.
Li, Zehan, Iqra Ameer, Yan Hu, et al.. (2023). Suicide Tendency Prediction from Psychiatric Notes Using Transformer Models. 481–483. 2 indexed citations
12.
Ameer, Iqra, Grigori Sidorov, Helena Gómez-Adorno, & Rao Muhammad Adeel Nawab. (2022). Multi-Label Emotion Classification on Code-Mixed Text: Data and Methods. IEEE Access. 10. 8779–8789. 31 indexed citations
13.
Ameer, Iqra, et al.. (2022). Multi-label emotion classification in texts using transfer learning. Expert Systems with Applications. 213. 118534–118534. 67 indexed citations
14.
Ameer, Iqra, et al.. (2020). Multi-label Emotion Classification using Content-Based Features in Twitter. Computación y Sistemas. 24(3). 20 indexed citations
15.
Ameer, Iqra, et al.. (2019). Bots and Gender Profiling on Twitter.. CLEF (Working Notes). 1 indexed citations
17.
Ameer, Iqra, Grigori Sidorov, & Rao Muhammad Adeel Nawab. (2019). Author profiling for age and gender using combinations of features of various types. Journal of Intelligent & Fuzzy Systems. 36(5). 4833–4843. 11 indexed citations
18.
Ameer, Iqra, et al.. (2018). Multi-lingual Author Profiling using Stylistic Features.. 240–246. 1 indexed citations
19.
Ameer, Iqra, et al.. (2015). Identification of Author Personality Traits using Stylistic Features Notebook for PAN at CLEF 2015. CLEF (Working Notes). 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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