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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Effectiveness of data-driven induction of semantic spaces and traditional classifiers for sarcasm detection
Mattia Antonino Di Gangi, Giosué Lo Bosco, Giovanni Pilato
Natural Language Engineering (2019) Vol. 25, Iss. 2, pp. 257-285
Open Access | Times Cited: 17

Showing 17 citing articles:

Automatic Classification of Sexism in Social Networks: An Empirical Study on Twitter Data
Francisco Rodríguez‐Sánchez, Jorge Carrillo‐de‐Albornoz, Laura Plaza
IEEE Access (2020) Vol. 8, pp. 219563-219576
Open Access | Times Cited: 90
Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using Deep Learning and BERT Model
Christopher Ifeanyi Eke, Azah Anir Norman, Liyana Shuib
IEEE Access (2021) Vol. 9, pp. 48501-48518
Open Access | Times Cited: 84
A Comprehensive Survey on Arabic Sarcasm Detection: Approaches, Challenges and Future Trends
Alaa Rahma, Shahira Shaaban Azab, Ammar Mohammed
IEEE Access (2023) Vol. 11, pp. 18261-18280
Open Access | Times Cited: 20
AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders
Diego D. Díaz‐Guerra, Marena Hernández Lugo, Yunier Broche‐Pérez, et al.
Frontiers in Psychiatry (2025) Vol. 15
Open Access
A Multi-Dimension Question Answering Network for Sarcasm Detection
Yufeng Diao, Hongfei Lin, Liang Yang, et al.
IEEE Access (2020) Vol. 8, pp. 135152-135161
Open Access | Times Cited: 28
Deep Neural Attention-Based Model for the Evaluation of Italian Sentences Complexity
Daniele Schicchi, Giovanni Pilato, Giosué Lo Bosco
(2020), pp. 253-256
Open Access | Times Cited: 22
Sarcasm Over Time and Across Platforms: Does the Way We Express Sarcasm Change?
Mondher Bouazizi, Tomoaki Ohtsuki
IEEE Access (2022) Vol. 10, pp. 55958-55987
Open Access | Times Cited: 8
DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages
Giosué Lo Bosco, Giovanni Pilato, Daniele Schicchi
Array (2021) Vol. 12, pp. 100097-100097
Open Access | Times Cited: 11
Multi-class Text Complexity Evaluation via Deep Neural Networks
Alfredo Cuzzocrea, Giosué Lo Bosco, Giovanni Pilato, et al.
Lecture notes in computer science (2019), pp. 313-322
Closed Access | Times Cited: 10
A comparison of latent semantic analysis and correspondence analysis of document-term matrices
Qianqian Qi, David J. Hessen, Tejaswini Deoskar, et al.
Natural Language Engineering (2023) Vol. 30, Iss. 4, pp. 722-752
Open Access | Times Cited: 3
Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora
Teresa Alcamo, Alfredo Cuzzocrea, Giosué Lo Bosco, et al.
(2020) Vol. 28, pp. 91-96
Closed Access | Times Cited: 7
Attention-based Model for Evaluating the Complexity of Sentences in English Language
Daniele Schicchi, Giovanni Pilato, Giosué Lo Bosco
(2020), pp. 221-225
Closed Access | Times Cited: 6
Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus
Dongyan Chen
Mathematical Problems in Engineering (2022) Vol. 2022, pp. 1-11
Open Access | Times Cited: 4
A Novel Approach for Supporting Italian Satire Detection Through Deep Learning
Gabriella Casalino, Alfredo Cuzzocrea, Giosué Lo Bosco, et al.
Lecture notes in computer science (2021), pp. 170-181
Closed Access | Times Cited: 4
A Controllable Text Simplification System for the Italian Language
Angelo Luigi Megna, Daniele Schicchi, Giosué Lo Bosco, et al.
(2021), pp. 191-194
Closed Access | Times Cited: 3
How are you? How a Robot can Learn to Express its own Roboceptions
Irene Trifirò, Agnese Augello, Umberto Maniscalco, et al.
Procedia Computer Science (2020) Vol. 176, pp. 480-489
Open Access | Times Cited: 1
Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology and Framework
Alfredo Cuzzocrea, Alfredo Cuzzocrea
(2021) Vol. 2021, Iss. 2, pp. 1-6
Open Access
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