SemEval-2018 Task 1: Affect in Tweets
Open accessAbstract
We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet.For each task, we created labeled data from English, Arabic, and Spanish tweets.The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification.Seventy-five teams (about 200 team members) participated in the shared task.We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful.We also analyze systems for consistent bias towards a particular race or gender.The data is made freely available to further improve our understanding of how people convey emotions through language.
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Most existing models for MLEC do not capture emotion-specific associations that can be useful for the classification of emotions.
“However, most existing models for MLEC do not capture emotion-specific associations that can be useful for the classification of emotions [7], [9], [10].”
From Emotion Classification With Visibility Graphs (2025) · cites this paper as [9]
Section 6.2 (page unavailable) says task-specific affect-lexicon features can improve emotion-related models. It does not say that most existing multi-label emotion classification models fail to capture emotion-specific associations; indeed, it reports that most top-performing systems used affect-lexicon features. (LLM, conf=0.78)
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334 · see citing works
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646 · see citing works
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793 · see citing works
Core affect and the psychological construction of emotion.
Russell, J. A. (2003) · Psychological Review · doi:10.1037/0033-295x.110.1.145 · Cited by 6,113 · see citing works
CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON
Mohammad, S. M., & Turney, P. D. (2012) · Computational Intelligence · doi:10.1111/j.1467-8640.2012.00460.x · Cited by 2,757 · see citing works
Self-citationFrijda, N. H. (1988) · American Psychologist · doi:10.1037//0003-066x.43.5.349 · Cited by 2,611 · see citing works
A GENERAL PSYCHOEVOLUTIONARY THEORY OF EMOTION
Plutchik, R. (1980) · Elsevier eBooks · doi:10.1016/b978-0-12-558701-3.50007-7 · Cited by 2,240 · see citing works
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
The theory of the estimation of test reliability
Kuder, G. F., & Richardson, M. W. (1937) · Psychometrika · doi:10.1007/bf02288391 · Cited by 1,953 · see citing works
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works
(1999) · Cited by 1,435 · see citing works
Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015) · arXiv · doi:10.48550/arxiv.1506.06726 · Cited by 1,293 · see citing works
Sentiment Analysis of Short Informal Texts
Kiritchenko, S., Zhu, X., & Mohammad, S. M. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4272 · Cited by 884 · see citing works
Self-citationA new ANEW: Evaluation of a word list for sentiment analysis in microblogsPreprint
Nielsen, F. Å. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.2903 · Cited by 733 · see citing works
Felbo, B., Mislove, A. E., Søgaard, A., Rahwan, I., & Lehmann, S. (2017) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 712 · see citing works
Discrimination-aware data mining
Pedreshi, D., Ruggieri, S., & Turini, F. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401959 · Cited by 676 · see citing works
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words
Mohammad, S. M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1017 · Cited by 616 · see citing works
Self-citationStance and Sentiment in Tweets
Mohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017) · ACM Transactions on Internet Technology · doi:10.1145/3003433 · Cited by 449 · see citing works
Self-citationMohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391 · see citing works
Self-citationExamining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Kiritchenko, S., & Mohammad, S. (2018) · Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s18-2005 · Cited by 384 · see citing works
Self-citationLearning to Generate Reviews and Discovering Sentiment
Radford, A., Jozefowicz, R., & Sutskever, I. (2017) · arXiv · doi:10.48550/arxiv.1704.01444 · Cited by 330 · cited 1× · see citing works
“• Sentiment neurons (Radford et al., 2017): a byte-level recurrent language model for learning sentence representations.”
BackgroundPalau, R. M., & Moens, M. (2009) · International Conference on Artificial Intelligence and Law · doi:10.1145/1568234.1568246 · Cited by 321 · see citing works
A Methodology for Direct and Indirect Discrimination Prevention in Data Mining
Hajian, S., & Domingo-Ferrer, J. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.72 · Cited by 312 · see citing works
WASSA-2017 Shared Task on Emotion Intensity
Mohammad, S. M., & Bravo-Márquez, F. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5205 · Cited by 284 · see citing works
Self-citationA Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining
Badaro, G., Baly, R., Hajj, H. M., Habash, N. Y., & El-Hajj, W. (2014) · Arabic Natural Language Processing Workshop (WANLP) · doi:10.3115/v1/w14-3623 · Cited by 183 · see citing works
Mohammad, S. M. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/58igjzkfg9k5 · Cited by 179 · see citing works
Self-citationBest-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
Kiritchenko, S., & Mohammad, S. M. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2074 · Cited by 174 · see citing works
Self-citationA survey on measuring indirect discrimination in machine learningPreprint
Žliobaitė, I. (2015) · arXiv.org · doi:10.48550/arxiv.1511.00148 · Cited by 157 · see citing works
Empirical Study of Machine Learning Based Approach for Opinion Mining in Tweets
Sidorov, G., Miranda‐Jiménez, S., Viveros-Jiménez, F., Gelbukh, A. F., Castro-Sánchez, N. A., Velásquez, F., Rangel, I. D., Suárez-Guerra, S., Treviño, A., & Gordon, J. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37807-2_1 · Cited by 124 · see citing works
Ultradense Word Embeddings by Orthogonal Transformation
Rothe, S., Ebert, S., & Schütze, H. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1091 · Cited by 117 · see citing works
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling
Kiritchenko, S., & Mohammad, S. M. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1095 · Cited by 113 · see citing works
Self-citationUnderstanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories
Mohammad, S. M., & Kiritchenko, S. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5or8sy3gqobf · Cited by 96 · see citing works
Self-citationBest-worst scaling: theory and methods
Marley, A. A. J. (2024) · Edward Elgar Publishing eBooks · doi:10.4337/9781800375635.00014 · Cited by 94 · see citing works
Sentiment Lexicons for Arabic Social Media
Mohammad, S. M., Salameh, M., & Kiritchenko, S. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/28kytu8w5m87 · Cited by 79 · see citing works
Self-citationA case study of the splithalf reliability coefficient.
Cronbach, L. J. (1946) · Journal of Educational Psychology · doi:10.1037/h0054328 · Cited by 71 · see citing works
A Methodology For Direct and Indirect Discrimination Prevention In Data Mining
kumar, V. R. (2013) · International Journal of MC Square Scientific Research · doi:10.20894/ijmsr.117.005.001.005 · Cited by 58 · see citing works
ML-SentiCon: un lexicón multilingüe de polaridades semánticas a nivel de lemas
Mata, F. C., Troyano, J. A., Pontes, B., & Rodríguez, F. J. O. (2014) · idUS (Universidad de Sevilla) · Cited by 13 · see citing works
Quantifying Qualitative Data for Understanding Controversial Issues
Wojatzki, M., Mohammad, S. M., Zesch, T., & Kiritchenko, S. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5kuqdkbfxhb3 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
3 of the 42 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- W Parrot. 2001. Emotions in Social Psychology. Psy- chology Press.
Cited by
Sorted by citations
Transformer models for text-based emotion detection: a review of BERT-based approaches
Acheampong, F. A., Nunoo‐Mensah, H., & Chen, W. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09958-2 · Cited by 686 · see citing works
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words
Mohammad, S. M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1017 · Cited by 616 · see citing works
Self-citationARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
Abdul-Mageed, M., Elmadany, A., & Nagoudi, E. M. B. (2021) · doi:10.18653/v1/2021.acl-long.551 · Cited by 405 · see citing works
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Kiritchenko, S., & Mohammad, S. (2018) · Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s18-2005 · Cited by 384 · see citing works
Self-citationGarcia, K., & Berton, L. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107057 · Cited by 369 · see citing works
Deep learning for affective computing: Text-based emotion recognition in decision support
Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S., & Prendinger, H. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.09.002 · Cited by 371 · see citing works
GPT is an effective tool for multilingual psychological text analysis
Rathje, S., Mirea, D.-M., Sucholutsky, I., Marjieh, R., Robertson, C. E., & Van Bavel, J. J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308950121 · Cited by 297 · see citing works
A survey of state-of-the-art approaches for emotion recognition in text
Alswaidan, N., & Menaï, M. E. B. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01449-0 · Cited by 279 · see citing works
WASSA-2017 Shared Task on Emotion Intensity
Mohammad, S. M., & Bravo-Márquez, F. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5205 · Cited by 284 · see citing works
Self-citationRecent advances in deep learning based dialogue systems: a systematic survey
Ni, J., Young, T., Pandelea, V., Xue, F., & Cambria, E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10248-8 · Cited by 279 · see citing works
TimeLMs: Diachronic Language Models from Twitter
Loureiro, D., Barbieri, F., Neves, L., Espinosa Anke, L., & Camacho-collados, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2022.acl-demo.25 · Cited by 263 · see citing works
Sentiment analysis classification system using hybrid BERT models
Talaat, A. S. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00781-w · Cited by 229 · see citing works
A survey on empathetic dialogue systems
Ma, Y., Nguyen, K. L., Xing, F., & Cambria, E. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.011 · Cited by 225 · see citing works
Lou, C., Tan, S.-S., & Chen, X. (2019) · Journal of Interactive Advertising · doi:10.1080/15252019.2019.1667928 · Cited by 231 · see citing works
A comprehensive survey of arabic sentiment analysis
Al‐Ayyoub, M., Khamaiseh, A. A., Jararweh, Y., & Al-Kabi, M. N. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.07.006 · Cited by 204 · see citing works
A Survey of Textual Emotion Recognition and Its Challenges
Deng, J., & Ren, F. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3053275 · Cited by 201 · see citing works
Tuncer, T., Doğan, Ş., & Acharya, U. R. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106547 · Cited by 157 · see citing works
An effective ensemble deep learning framework for text classification
Mohammed, A., & Kora, R. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.11.001 · Cited by 169 · see citing works
Khan, L., Amjad, A., Afaq, K. M., & Chang, H.-T. (2022) · Applied Sciences · doi:10.3390/app12052694 · Cited by 171 · see citing works
A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets
Jabreel, M., & Moreno, A. (2019) · Applied Sciences · doi:10.3390/app9061123 · Cited by 153 · see citing works
Latent Emotion Memory for Multi-Label Emotion Classification
Fei, H., Zhang, Y., Ren, Y., & Ji, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6271 · Cited by 144 · see citing works
Baziotis, C., Nikolaos, A., Chronopoulou, A., Kolovou, A., Paraskevopoulos, G., Ellinas, N., Narayanan, S., & Potamianos, A. (2018) · Proceedings of The 12th International Workshop on Semantic Evaluation · doi:10.18653/v1/s18-1037 · Cited by 142 · see citing works
Li, Z., Zhu, H., Lu, Z., & Yin, M. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.647 · Cited by 139 · see citing works
SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
Yin, D., Meng, T., & Chang, K.-W. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.341 · Cited by 150 · see citing works
Multi-label emotion classification in texts using transfer learning
Ameer, I., Bölücü, N., Siddiqui, M. H. F., Can, B., Sidorov, G., & Gelbukh, A. F. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118534 · Cited by 138 · see citing works
TweetNLP: Cutting-Edge Natural Language Processing for Social Media
Camacho-collados, J., Rezaee, K., Riahi, T., Ushio, A., Loureiro, D., Antypas, D., Boisson, J., Espinosa Anke, L., Liu, F., & Martínez Cámara, E. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2022.emnlp-demos.5 · Cited by 142 · see citing works
Islam, M. S., Kabir, M. N., Ghani, N. A., Zamli, K. Z., Zulkifli, N. S. A., Rahman, M. M., & Moni, M. A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10651-9 · Cited by 170 · see citing works
Deep contextualized word representations for detecting sarcasm and irony
Ilić, S., Marrese-Taylor, E., Balazs, J., & Matsuo, Y. (2018) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w18-6202 · Cited by 126 · see citing works
Opinion mining and emotion recognition applied to learning environments
Estrada, M. L. B., Cabada, R. Z., Bustillos, R. O., & Graff, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113265 · Cited by 115 · see citing works
A panoramic survey of natural language processing in the Arab world
Darwish, K. M., Habash, N. Y., Abbas, M., Al-Khalifa, H. S., Al-Natsheh, H. T., Bouamor, H., Bouzoubaa, K., Cavalli‐Sforza, V., El-Beltagy, S. R., El-Hajj, W., et al. (2021) · Communications of the ACM · doi:10.1145/3447735 · Cited by 130 · see citing works
Investigating Gender Bias in BERT
Bhardwaj, R., Majumder, N., & Poria, S. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09881-2 · Cited by 112 · see citing works
SEDAT: Sentiment and Emotion Detection in Arabic Text Using CNN-LSTM Deep Learning
Abdullah, M., Hadzikadicy, M., & Shaikhz, S. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00134 · Cited by 111 · see citing works
Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis
Mohammad, S. M. (2022) · Computational Linguistics · doi:10.1162/coli_a_00433 · Cited by 112 · see citing works
Self-citationBorrow from rich cousin: transfer learning for emotion detection using cross lingual embedding
Ahmad, Z., Jindal, R., Ekbal, A., & Bhattachharyya, P. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112851 · Cited by 109 · see citing works
Challenges and Opportunities of Text-Based Emotion Detection: A Survey
Maruf, A. A., Khanam, F., Haque, M. M., Jiyad, Z. M., Mridha, M. F., & Aung, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3356357 · Cited by 118 · see citing works
Emotion Correlation Mining Through Deep Learning Models on Natural Language Text
Wang, X., Kou, L., Sugumaran, V., Luo, X., & Zhang, H. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2987064 · Cited by 101 · see citing works
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
Zhong, R., Lee, K., Zhang, Z., & Klein, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.244 · Cited by 118 · see citing works
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories
Mohammad, S. M., & Kiritchenko, S. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5or8sy3gqobf · Cited by 96 · see citing works
Self-citationA Survey of Cutting-edge Multimodal Sentiment Analysis
Singh, U., Abhishek, K., & Azad, H. K. (2024) · ACM Computing Surveys · doi:10.1145/3652149 · Cited by 99 · see citing works
Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor
Nissim, M., van Noord, R., & van der Goot, R. (2020) · Computational Linguistics · doi:10.1162/coli_a_00379 · Cited by 91 · see citing works
On the evaluation and combination of state-of-the-art features in Twitter sentiment analysis
Carvalho, J., & Plastino, A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09895-6 · Cited by 87 · see citing works
SRL-ACO: A text augmentation framework based on semantic role labeling and ant colony optimization
Onan, A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101611 · Cited by 86 · see citing works
Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology
Graterol, W., Díaz-Amado, J., Cardinale, Y., Dongo, I., Lopes-Silva, E., & Libarino, C. S. (2021) · Sensors · doi:10.3390/s21041322 · Cited by 84 · see citing works
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
Chiril, P., Pamungkas, E. W., Benamara, F., Moriceau, V., & Patti, V. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09862-5 · Cited by 87 · see citing works
A Comparison of Word-Embeddings in Emotion Detection from Text using BiLSTM, CNN and Self-Attention
Polignano, M., Basile, P., de Gemmis, M., & Semeraro, G. M. (2019) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3314183.3324983 · Cited by 82 · see citing works
Min, H., Peng, Y., Shoss, M. K., & Yang, B. (2021) · Journal of Applied Psychology · doi:10.1037/apl0000886 · Cited by 80 · see citing works
Liu, Z., Yang, K., Xie, Q., Zhang, T., & Ananiadou, S. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671552 · Cited by 90 · see citing works
A Survey of Opinion Mining in Arabic
Badaro, G., Baly, R., Hajj, H. M., El-Hajj, W., Shaban, K. B., Habash, N. Y., Sallab, A. A. A., & Hamdi, A. (2019) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3295662 · Cited by 77 · see citing works
Onan, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120908 · Cited by 76 · see citing works
Yu, J., Marujo, L., Jiang, J., Karuturi, P., & Brendel, W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1137 · Cited by 75 · see citing works
Huang, C., Trabelsi, A., & Zai͏̈ane, O. R. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2006 · Cited by 81 · see citing works
Comprehensive review and comparative analysis of transformer models in sentiment analysis
Bashiri, H., & Naderi, H. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02214-3 · Cited by 92 · see citing works
EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets
Kabir, M. Y., & Madria, S. (2021) · Online Social Networks and Media · doi:10.1016/j.osnem.2021.100135 · Cited by 73 · see citing works
Czarnowska, P., Vyas, Y., & Shah, K. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00425 · Cited by 72 · see citing works
Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities
Raamkumar, A. S., & Yang, Y. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3226693 · Cited by 75 · see citing works
Automatically Identifying Complaints in Social Media
Preoțiuc-Pietro, D., Găman, M., & Αλέτρας, Ν. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1495 · Cited by 68 · see citing works
Emotion analysis of Arabic tweets using deep learning approach
Baali, M., & Ghneim, N. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0252-x · Cited by 68 · see citing works
hULMonA: The Universal Language Model in Arabic
Jundi, O. E., Antoun, W., Droubi, N. E., Hajj, H. M., El-Hajj, W., & Shaban, K. B. (2019) · Arabic Natural Language Processing Workshop (WANLP) · doi:10.18653/v1/w19-4608 · Cited by 71 · see citing works
Afzal, S., Khan, H. A., Piran, M. J., & Lee, J. W. (2024) · IEEE Access · doi:10.1109/access.2024.3422480 · Cited by 75 · see citing works
IEST: WASSA-2018 Implicit Emotions Shared Task
Klinger, R., Clercq, O. D., Mohammad, S. M., & Balahur, A. (2018) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w18-6206 · Cited by 67 · see citing works
Self-citationTopic-Enhanced Capsule Network for Multi-Label Emotion Classification
Fei, H., Ji, D., Zhang, Y., & Ren, Y. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3001390 · Cited by 63 · see citing works
An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity
Araque, Ó., & Iglesias, C. Á. (2020) · IEEE Access · doi:10.1109/access.2020.2967219 · Cited by 63 · see citing works
Multi-Label Emotion Classification on Code-Mixed Text: Data and Methods
Ameer, I., Sidorov, G., Gómez-Adorno, H., & Nawab, R. M. A. (2022) · IEEE Access · doi:10.1109/access.2022.3143819 · Cited by 63 · see citing works
Emotion Recognition for Vietnamese Social Media Text
Ho, V. A., Nguyen, D. H.-C., Nguyen, D. H., Pham, L. T.-V., Nguyen, D.-V., Nguyen, K. V., & Nguyen, N. L.-T. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-6168-9_27 · Cited by 63 · see citing works
#lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19
Stella, M., Restocchi, V., & Deyne, S. D. (2020) · Big Data and Cognitive Computing · doi:10.3390/bdcc4020014 · Cited by 60 · see citing works
Deep learning for emotion analysis in Arabic tweets
Khalil, E. A. H., Houby, E. M. F. E., & Mohamed, H. K. (2021) · Journal Of Big Data · doi:10.1186/s40537-021-00523-w · Cited by 62 · see citing works
TWilBert: Pre-trained deep bidirectional transformers for Spanish Twitter
González, J. Á., Hurtado, L.-F., & Plà, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.09.078 · Cited by 61 · see citing works
Arabic sentiment analysis using recurrent neural networks: a review
Alhumoud, S. O., & Wazrah, A. A. A. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09989-9 · Cited by 61 · see citing works
WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art
Mohammad, S. M., & Kiritchenko, S. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/33oybkwsfa8r · Cited by 63 · see citing works
Self-citationChinese EmoBank: Building Valence-Arousal Resources for Dimensional Sentiment Analysis
Lee, L.-H., Li, J.-H., & Yu, L.-C. (2022) · ACM Trans. Asian Low Resour. Lang. Inf. Process. · doi:10.1145/3489141 · Cited by 81 · see citing works
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic
Badaro, G., El Jundi, O., Khaddaj, A., Maarouf, A., Kain, R., Hajj, H., & El-Hajj, W. (2018) · Proceedings of The 12th International Workshop on Semantic Evaluation · doi:10.18653/v1/s18-1036 · Cited by 60 · see citing works
Sentiment Analysis for Social Media
Iglesias, C. Á., & Moreno, A. (2019) · Applied Sciences · doi:10.3390/app9235037 · Cited by 59 · see citing works
GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP
Khondaker, M. T. I., Waheed, A., Nagoudi, E. M. B., & Abdul-Mageed, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.16 · Cited by 79 · see citing works
EmoDet2: Emotion Detection in English Textual Dialogue using BERT and BiLSTM Models
Al-Omari, H., Abdullah, M., & Shaikh, S. (2020) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/icics49469.2020.239539 · Cited by 59 · see citing works
DepecheMood++: A Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques
Araque, Ó., Gatti, L., Staiano, J., & Guerini, M. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2934444 · Cited by 58 · see citing works
XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection
Ohman, E. S., Pàmies, M., Kajava, K., & Tiedemann, J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.575 · Cited by 61 · see citing works
Firdaus, M., Chauhan, H., Ekbal, A., & Bhattacharyya, P. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.393 · Cited by 56 · see citing works
TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning
Abdullah, M., & Shaikh, S. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1053 · Cited by 57 · see citing works
Improved emotion recognition in Spanish social media through incorporation of lexical knowledge
Plaza-del-Arco, F., Martín-Valdivia, M., Ureña-López, L., & Mitkov, R. (2020) · Future generations computer systems · doi:10.1016/j.future.2019.09.034 · Cited by 49 · see citing works
Kim, T. S., Lee, Y., Chang, M., & Kim, J. (2023) · Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3586183.3606833 · Cited by 56 · see citing works
Wunderlich, F., & Memmert, D. (2020) · Applied Sciences · doi:10.3390/app10020431 · Cited by 55 · see citing works
Collective emotions during the COVID-19 outbreak.
Metzler, H., Rimé, B., Pellert, M., Niederkrotenthaler, T., Natale, A. D., & García, D. (2022) · Emotion · doi:10.1037/emo0001111 · Cited by 52 · see citing works
AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection
Awal, R., Cao, R., Lee, R. K.-W., & Mitrović, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75762-5_55 · Cited by 56 · see citing works
SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets
Duppada, V., Jain, R., & Hiray, S. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1002 · Cited by 51 · see citing works
EmoEvent: A Multilingual Emotion Corpus based on different Events
Plaza-del-Arco, F. M., Strapparava, C., López, L. A. U., & Valdivia, M. T. M. (2020) · Language Resources and Evaluation · Cited by 50 · see citing works
Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine
Du, J., Luo, C., Shegog, R., Bian, J., Cunningham, R. M., Boom, J. A., Poland, G. A., Chen, Y., & Tao, C. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.22025 · Cited by 50 · see citing works
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags
Park, J. H., Xu, P., & Fung, P. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1039 · Cited by 50 · see citing works
Hybrid Feature Model for Emotion Recognition in Arabic Text
Alswaidan, N., & Menaï, M. E. B. (2020) · IEEE Access · doi:10.1109/access.2020.2975906 · Cited by 51 · see citing works
Kim, Y., Lee, H., & Jung, K. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1019 · Cited by 49 · see citing works
Troiano, E., Oberländer, L., & Klinger, R. (2022) · Computational Linguistics · doi:10.1162/coli_a_00461 · Cited by 54 · see citing works
A Context Integrated Model for Multi-label Emotion Detection
Samy, A. E., El-Beltagy, S. R., & Hassanien, E. E. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.461 · Cited by 48 · see citing works
Cao, Y. T., & Daumé, H. (2021) · Computational Linguistics · doi:10.1162/coli_a_00413 · Cited by 47 · see citing works
Textual emotion detection in health: Advances and applications
Saffar, A. H., Mann, T. K., & Ofoghi, B. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104258 · Cited by 48 · see citing works
Bostan, L. A. M., Kim, E., & Klinger, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03184 · Cited by 46 · see citing works
HeBERT and HebEMO: A Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition
Chriqui, A., & Yahav, I. (2022) · INFORMS Journal on Data Science · doi:10.1287/ijds.2022.0016 · Cited by 52 · see citing works
How Dramatic Events Can Affect Emotionality in Social Posting: The Impact of COVID-19 on Reddit
Basile, V., Cauteruccio, F., & Giorgio Terracina, B. (2021) · Future Internet · doi:10.3390/fi13020029 · Cited by 44 · see citing works
Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis
Bhaskaran, J., & Bhallamudi, I. (2019) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/w19-3809 · Cited by 45 · see citing works
The Evolution of Language Models Applied to Emotion Analysis of Arabic Tweets
Al-Twairesh, N. (2021) · Information · doi:10.3390/info12020084 · Cited by 43 · see citing works
Practical Text Classification With Large Pre-Trained Language ModelsPreprint
Kant, N., Puri, R., Yakovenko, N., & Catanzaro, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.01207 · Cited by 42 · see citing works
Interactive double states emotion cell model for textual dialogue emotion prediction
Li, D., Li, Y., & Wang, S. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105084 · Cited by 42 · see citing works
- Authors
- Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S.
- Affiliations
- Carnegie Mellon University Qatar (QA)
- National Research Council Canada (CA)
- Venue
- Proceedings of The 12th International Workshop on Semantic Evaluation
- Published
- 2018
- Pages
- 1-17
- OpenAlex
- W2805744755
- Cited by
- 746
- References
- 42
- Citations by year
- 2026: 49 · 2025: 64 · 2024: 85 · 2023: 104 · 2022: 98 · 2021: 107 · 2020: 87 · 2019: 81 · 2018: 70 · 2017: 1
- FWCI
- 35.21
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Workshop on Semantic Evaluation (SemEval) (publishedVersion, cc-by)
- NPARC (submittedVersion, cc-by)
- INFM-OAR (INFN Catania) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- affective computing, emotion recognition, tweets, social media text, sentiment analysis, emotion classification, shared task, Arabic tweets, Spanish tweets, English-language tweets, algorithmic bias, gender bias, racial bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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