SemEval-2018 Task 1: Affect in Tweets

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

A circumplex model of affect.

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An argument for basic emotions

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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

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

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 2,901

The laws of emotion.

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Fair prediction with disparate impact: a study of bias in recidivism prediction instruments

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The theory of the estimation of test reliability

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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

AI Deep Learning with Multiple Labels for Sentiment Classification of Tweets

Gao, Z., & Chen, C.-P. (2019) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2019.8702139 · Cited by 1

GWU NLP Lab at SemEval-2019 Task 3 : EmoContext: Effectiveness ofContextual Information in Models for Emotion Detection inSentence-level at Multi-genre Corpus

Tafreshi, S., & Diab, M. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2038 · Cited by 1

Application of Lovheim Model for Emotion Detection in English Tweets.

Fornacciari, P., Cagnoni, S., Mordonini, M., Tarollo, L., & Tomaiuolo, M. (2019) · Cited by 1

A Case Study in Multi-Emotion Classification via Twitter

Ibrahiem, S. S., Ismail, S., Bahnasy, K., & Aref, M. M. (2020) · doi:10.1109/iceeng45378.2020.9171768 · Cited by 1

Sensitivity of Arabic Sentiment Analysis Tools

Conlon, B., & Brenner, P. R. (2020) · International Conference on Social Networks Analysis, Management and Security (SNAMS) · doi:10.1109/snams52053.2020.9336535 · Cited by 1

Classifying Judgements using Transfer Learning

Parameswaran, P., Trotman, A., Liesaputra, V., & Eyers, D. M. (2020) · Cited by 1

Human Behavior Assessment using Ensemble Models

Khilji, A. F. U. R., Khaund, R., & Sinha, U. (2020) · Cited by 1

CovidStream: Interactive Visualization of Emotions Evolution Associated with Covid-19

Baca, H. A. H., Valdivia, F. D. L. P., Atencio, Y. T. P., Ibarra, M. J., Cruz, M. A., & Baca, M. E. H. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-76228-5_39 · Cited by 1

How to Estimate Continuous Sentiments From Texts Using Binary Training Data

Wankmüller, S., & Heumann, C. (2021) · Cited by 1

A Custom State LSTM Cell for Text Classification Tasks

Haralabopoulos, G., & Anagnostopoulos, I. E. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-08223-8_40 · Cited by 1

ReferEmo: A Referential Quasi-multimodal Model for Multilabel Emotion Classification

Esperança, Á., & Luo, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12423-5_27 · Cited by 1

An Analysis of People’s Emotional Change Toward Vaccines and Its Factors in the Corona Disaster

Fukuda, S., Nanba, H., & Shoji, H. (2022) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.34.3_592 · Cited by 1

Analysis of International Public Emotional Responses Toward the COVID-19 Vaccine

Fukuda, S., Nanba, H., & Shoji, H. (2022) · IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) · doi:10.1109/iiaiaai55812.2022.00024 · Cited by 1

MINDFUL SPACE IN SENTENCES - A DATASET OF VIRTUAL EMOTIONS FOR NATURAL LANGUAGE CLASSIFICATION

Tu, H., & YANG, C. (2023) · Architecture and Planning Journal (APJ) · doi:10.54729/2789-8547.1238

Experiencer-Specific Emotion and Appraisal Prediction

Wegge, M., Troiano, E., Oberländer, L., & Klinger, R. (2022) · Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) · doi:10.18653/v1/2022.nlpcss-1.3 · Cited by 1

Emotion Detection using Data Driven ModelsPreprint

S, N. K., Vinayakumar, R., & Soman, K. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.03141 · Cited by 1

Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion DetectionPreprint

Rathnayaka, P., Abeysinghe, S., Samarajeewa, C., Manchanayake, I., & Walpola, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.01452 · Cited by 2

#lockdown: network-enhanced emotional profiling at the times of COVID-19Preprint

Stella, M., Restocchi, V., & Deyne, S. D. (2020) · arXiv.org · doi:10.48550/arxiv.2005.04404 · Cited by 53

Towards humanized open-domain conversational agents

Zhong, P. (2021) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/147791 · Cited by 1

Attention-based Approaches for Text Analytics in Social Media and Automatic Summarization

Barba, J. Á. G. (2021) · doi:10.4995/thesis/10251/172245 · Cited by 1

Two is better than one: Using a single emotion lexicon can lead to unreliable conclusionsPreprint

Czarnek, G., & Stillwell, D. J. (2022) · doi:10.31234/osf.io/7k5sn · Cited by 1

SentiMental: An emotional profiling algorithm for identifying affect patterns in textPreprint

Stella, M. (2018) · doi:10.31219/osf.io/cun5x · Cited by 1

Multi-label emotion classification of Tweets with transformer modelsPreprint

Hettikankanamge, T. L., & Pinidiyaarachchi, A. J. (2023) · Research Square · doi:10.21203/rs.3.rs-2583392/v1 · Cited by 1

Analyzing the Overturning of Roe vs Wade on Twitter using Natural Language Processing Techniques

Pinto, G. (2023) · doi:10.36837/chapman.000485 · Cited by 1

Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase EnrichmentPreprint

Nayak, K. S. R., & Kosseim, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4881917 · Cited by 1

DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction

Kim, Y., & Lee, H. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1044

IIT Delhi at SemEval-2018 Task 1 : Emotion Intensity Prediction

Kotakonda, B., Gowda, P., & Lall, B. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1051

RIDDL at SemEval-2018 Task 1: Rage Intensity Detection with Deep Learning

Elango, V., & Uppal, K. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1054

Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets

Chi, Z., Huang, H., Chen, J., Wu, H., & Wei, R. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1046

KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets

Nyegaard-Signori, T., Helms, C. V., Bjerva, J., & Augenstein, I. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1058

uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network

Orabi, A. H., Orabi, M. H., Inkpen, D. Z., & Bruwaene, D. V. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1027

結合卷積神經網路與遞迴神經網路於推文極性分類 (Combining Convolutional Neural Network and Recurrent Neural Network for Tweet Polarity Classification) [In Chinese]

Yeh, C.-T., & Chen, C.-P. (2018) · International Conference on Computational Linguistics

GenSMT at SemEval-2019 Task 3: Contextual Emotion Detection in tweets using multi task generic approach

Dumitru, B. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2037

A Simple and Effective Approach for Fine Tuning Pre-trained Word Embeddings for Improved Text ClassificationPreprint

Al-Khatib, A., & El-Beltagy, S. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.02579 · Cited by 2

Periodicity Detection of Emotional Communities in Microblogging

Loglisci, C. A. A. M. D., & Malerba, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35166-3_39

Semi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification.

Xu, J., Masuda, K., Nishizaki, H., Fukumoto, F., & Suzuki, Y. (2020) · Language Resources and Evaluation · Cited by 1

Affect inTweets: A Transfer Learning Approach.

Zhang, L., Huang, H.-L., Yu, Y., & MOLDOVAN, D. I. (2020) · Language Resources and Evaluation

Automatic Detection of Emotions and Distress in Textual Data

Mohammadi, E. (2019) · Spectrum Research Repository (Concordia University)

Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data LimitationsPreprint

Buechel, S., Sedoc, J., Schwartz, H. A., & Ungar, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.10949 · Cited by 1

Opinion Analysis Corpora Across Languages

Seki, Y. (2020) · The information retrieval series · doi:10.1007/978-981-15-5554-1_6

Trend analysis in online data with unsupervised classification and appraisal categories

Dragos, V., Besombes, J., & Mascaro, A. (2020) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion45008.2020.9190451

Predicting movie-elicited emotions from dialogue in screenplay text: A study on “Forrest Gump”

Iavarone, B., & Dell’Orletta⋄, F. (2020) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.8610

DCH-2: A Parallel Customer-Helpdesk Dialogue Corpus with Distributions of Annotators' LabelsPreprint

Zeng, Z., & Sakai, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08755

Multi-dimensional Affect in Poetry (POCA) Dataset: Acquisition, Annotation and Baseline Results

Khan, A., Hopkins, J., & Güneş, H. (2021) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii52823.2021.9597451

Brain-Inspired Computing

Amunts, K., Grandinetti, L., Lippert, T., & Petkov, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-82427-3 · Cited by 1

Improving Korean Emotion Classification via Colloquial-Adaptive Pretraining

Lee, J., Kim, D., Ro, Y., & Kang, P. (2021) · Journal of Korean Institute of Industrial Engineers · doi:10.7232/jkiie.2021.47.4.342

Fine-grained sentiment analysis: a piece of cake?

Hoste, V. (2020) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.9010

Computational behavioral analytics: estimating psychological traits in foreign languages.

Reese, K. (2020) · ThinkIR: The University of Louisville's Institutional Repository (University of Louisville) · doi:10.18297/etd/3568

Association Extraction and Recognition of Multiple Emotion Expressed in Social Texts

Zou, J., Wu, S., Yang, Z., Chen, C., Sun, Y., Jiang, M., & Huang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06794-5_34

Análisis de la contribución de los fonemas a la predicción de la valencia emocional en tweets en español e inglés

Rojas, G. A. B. (2020) · doi:10.11144/javeriana.10554.51095

AraFranco: An Arabic Word Embedding Model for Representing Franco-Arabic User-Generated Text

Mansy, A., Rady, S., & Gharib, T. F. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-20601-6_48

EMOTION RECOGNITION BY TEXTUAL TWEETS USING VOTING CLASSIFIER (LR-SGD)

(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets34767

Emotionsklassifikation in Texten unter Berücksichtigung des Komponentenprozessmodells

Klinger, R. (2023) · Springer eBooks · doi:10.1007/978-3-662-65964-9_7

Adversarial Active Learning with Guided BERT Feature Encoding

Pang, L. X., Xie, K., Zhang, Y., Fleming, M., Xu, D. C., & Liu, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33377-4_39

Beyond the Picture: An Analysis of Emotional Attachment in Relation to Meaningful Natural Spaces.

Fahey, E. (2023) · doi:10.26686/wgtn.23394719

Análisis de elementos fonéticos y elementos emocionales para predecir la polaridad en fuentes de microblogging

Castillo, E. (2023) · doi:10.11144/javeriana.10554.63548 · Cited by 1

Inducing Generalizable and Interpretable Lexica

Geng, Y., Wu, Z., Santhosh, R., Srivastava, T., Ungar, L., & Sedoc, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.325

Should you marginalize over possible tokenizations?

Chirkova, N., Kruszewski, G., Rozen, J., & Dymetman, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.1 · Cited by 6

Findings of WASSA 2023 Shared Task: Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages

Ameer, I., Bölücü, N., Xu, H., & Bataineh, A. A. (2023) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/2023.wassa-1.56

Design and Optimization of Loss Functions in Fine-grained Sentiment and Emotion Analysis

Ye, S., Li, D., & Rinku, A. M. (2023) · doi:10.1109/icnlp58431.2023.00037 · Cited by 1

The Fourth Workshop on Insights from Negative Results in NLP

Tafreshi, S., Akula, A., Sedoc, J., Валентинович, Д. А., Rogers, A., & Rumshisky, A. (2023) · doi:10.18653/v1/2023.insights-1

Analysis of Emotional Changes in Twitter Users by Personality Type Before and After the Outbreak of the COVID-19 Pandemic

Matsumoto, K., Yoshida, M., & Kishima, R. (2023) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd59587.2023.10280850

On Enhancing Fine-Tuning for Pre-trained Language Models

Betka, A., Ferhat, Z., Barka, R., Boutiba, S., Kahhoul, Z. S., Tiar, M. L., Abdelalí, A., & Dahmani, H. (2023) · Arabic Natural Language Processing Conference (ArabicNLP) · doi:10.18653/v1/2023.arabicnlp-1.33 · Cited by 1

A Hybrid Approach for Spanish Emotion Recognition Applying Fuzzy Semantic Processing

Parets, O. M. G., Simón-Cuevas, A., Olivas, J. Á., & Perea‐Ortega, J. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49552-6_20

Online Behaviour Indicator Extraction for Enhanced Cancer Patient Management using Real-World Data

Pantalona, G., Barachanou, A., Loukas, N., Apostolidis, L., Tsalakanidou, F., & Papadopoulos, S. (2023) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3617233.3617264

Automated Text Selection for Raw Data Annotation

Saeed, S., Haider, A., & Bilal, K. (2023) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit60620.2023.00027

Estudios de lingüística aplicada VI

Molés-Cases, T., Móstoles, R. C., Periñán-Pascual, C., & Forteza, F. R. (2023) · doi:10.4995/sclin.2023.672001

Fine-Tuning BERT on Twitter and Reddit Data in Luganda and English

Kimera, R., Rim, D. N., & Choi, H. (2023) · International Conference on Natural Language Processing and Information Retrieval (NLPIR) · doi:10.1145/3639233.3639344

Analysis of the feelings in the reviews of patients versus the evaluation of the ease of use, effectiveness, and satisfaction of prescribed medications

Agudelo, C. A., & Zablah, I. (2024) · Bionatura journal : · doi:10.21931/bj/2024.01.01.24

Developing a Comprehensive Emotion Lexicon for Turkish

Ünal, E., Sağlam, F., & Genç, B. (2024) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-56310-2_13

Emotion Detection using Twitter Datasets and Spacy Algorithm

RAZZAQ, V. A., & PATAN, M. (2024) · International Journal of Engineering Technology and Management Sciences · doi:10.46647/ijetms.2024.v08i03.007

Analysis of International Interests and Emotional Responses to the Coronavirus Pandemic

Fukuda, S., Nanba, H., & Shoji, H. (2024) · Information Engineering Express · doi:10.52731/iee.v10.i1.804

Arabic Multiclass Emotional Analysis Using a Bidirectional GRU-LSTM Deep Model on Twitter

Elfaik, H., & Nfaoui, E. H. (2024) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset60544.2024.10548363

Sweeping the social minefield : detecting online opinions and emotions with NLP

Lemmens, J. (2024) · doi:10.63028/10067/2060990151162165141

A Fine-Tuned Distilled Zero-Shot Student Model for Emotion Detection in Academic-Related Responses

Canon, M. J. P. (2024) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2024.14.7.2130

Textual Backdoor Attack via Keyword Positioning

Chen, D., Mao, F., Jin, H., & Cui, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5609-4_5

Adequate Prompting Improves Performance of Regression Models of Emotional Content

Bulla, L., & Mongiovı̀, M. (2024) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3677525.3678653 · Cited by 1

Investigating Developer Emotions in Technical Chat Conversations: The Study of Discord and Slack

Rashid, R. B. (2024) · doi:10.22215/etd/2024-16119

Deep Learning and Transformers for Emotion Detection

Cavicchio, F. (2024) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-72047-5_6

Fusing Emoji Emotion Distribution for Multi-Label Emotion Classification

Chen, S.-F., Liu, Y., & Zeng, X. (2024) · doi:10.1109/icnlp60986.2024.10692928

Preservation of emotional context in tweet embeddings on social networking sites

Maruyama, O., Yoshinaga, A., & Sawai, K.-I. (2024) · Artificial Life and Robotics · doi:10.1007/s10015-024-00974-3

How are You Really Doing? Dig into the Wheel of Emotions with Large Language Models

Luca, M., Lopez, G., Longa, A., & Kaul, J. (2024) · Artificial Intelligence for Business (AIxB) · doi:10.1109/aixb62249.2024.00020

A Hybrid Approach for Sentiment Analysis of Arabic Tweets

Saidi, M., Shahi, K., Zare, N., & Hassanpoor, H. (2024) · International Conference on Signal Processing and Intelligent Systems (ICSPIS) · doi:10.1109/icspis65223.2024.10931080

Graph-Based Lexical Sentiment Propagation Algorithm

Kirigin, T. B., Babić, S. B., & Perаk, B. (2025) · Mathematics · doi:10.3390/math13071141

Social Media Multi-Emotion Detection

Ibrahiem, S. S., Mohammed, B. I. E.-S., Ahmed, M. S., & El-Bahnasy, K. (2025) · International Conference on Advanced Innovations in Smart Cities (ICAISC) · doi:10.1109/icaisc64594.2025.10959471

Advancing sentiment understanding and safety in NLP systems

Deng, Y. (2025) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/183610

Beyond Words: Exploring Emotional Contrasts Between Human and ChatGPT Responses in Medicine and Finance

Fatahi, S., Khalaj, M., Abhari, S., Vassileva, J., & Roy, C. K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93418-6_10

Sentiment, Volume, and Topics in University Tweets: Methodology, Insights, and Challenges

Câmpan, A., Truţă, T. M., & Karki, A. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-92178-0_6

Sentiment Analysis for Human Vocal Translator Using ML Techniques

Santhiya, P., Madheshwaran, J., Nair, M. H., & Rajasekar, P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-95017-9_24

Strategies for Class Imbalance in Emotion Analysis: A Comparative Evaluation

Soyalp, M., Yıldırım, G., & Keskinöz, M. (2025) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas66241.2025.11101974

Research on Sentiment Classification Based on Hybrid BERT Model and Hypergraph Attention Network

Dai, W., Zheng, D., Yu, F., & Yan, F. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-94892-3_25

Enhancing Sentiment Analysis Accuracy and Evaluating Task Affinity Using Large Language Models

Inoshita, K. (2025) · IEEE International Conference on Artificial Intelligence for Learning and Optimization (ICoAILO) · doi:10.1109/icoailo66760.2025.11155956

Enhancing Sentiment Analysis in Social Media Using Blockchain Technology: a Subject Matter Archetype Approach

Dey, R. K., Sarddar, D., Bose, R., Sutradhar, S., & Roy, S. (2025) · International Conference on Computing, Intelligence, and Application (CIACON) · doi:10.1109/ciacon65473.2025.11189471

Listening to Oral History: Emotion Annotation and Recognition in the ACT UP Oral History Project

Pessanha, F., Padovani, I., van Klaveren, J., Kaya, H., Salah, A. A. A., & Masthoff, J. (2025) · doi:10.1145/3746273.3760204

E2MoCase: A Dataset for Emotional, Event and Moral Observations in News Articles on High-impact Legal Cases

Greco, C. M., Zangari, L., Picca, D., & Tagarelli, A. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761618 · Cited by 3

Best-Fit Document: Enhancing Compositional Generalization in Multi-label Text Classification

Guo, H., Zhu, S., Wang, H., Chen, J., Li, J., Ma, R., & Wu, G. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3349-7_25

Weakening Prediction Confidence Makes Backdoors Strengthened: A Strong Textual Backdoor Attack on Prefix-tuning

Tan, Y., Zhang, H., & Sakuma, J. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227624

Semantically Guided Multi-Stage Graph Learning for Inductive Multi-Label Text Classification

Wu, M. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00380-0

A computational analysis of emotionally manipulative content in media coverage of the Russia-Ukraine war

Овсянко, О. Л., Prokopenko, A. V., & Zinchenko, A. (2025) · East European Journal of Psycholinguistics · doi:10.29038/ovs

EmoTweet-Malay-6: A Malay Tweet Corpus for Textual Emotion Analysis

Lim, Y. H., & Yan, J. L. S. (2026) · International Journal of Asian Language Processing · doi:10.1142/s2717554526500049

EmoIntens Tracker at SemEval-2018 Task 1: Emotional Intensity Levels in #Tweets

Turcu, R. A., Amarandei, S. M., Fleşcan-Lovin-Arseni, I. A., Gîfu, D., & Trandabăţ, D. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1026 · Cited by 3

CENNLP at SemEval-2018 Task 1: Constrained Vector Space Model in Affects in Tweets

R, N. J., B., B. G. H., Kumar, M. A., & Soman, K. P. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1049 · Cited by 1

Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets

Chi, Z., Huang, H., Chen, J., Wu, H., & Wei, R. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1046

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

SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets

Plaza-del-Arco, F. M., Jiménez-Zafra, S. M., Martín, M., & López, L. A. U. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1017 · Cited by 4