The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
WASSA-2017 Shared Task on Emotion Intensity
Open accessAbstract
We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet.We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best-worst scaling (BWS).We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity).The data was partitioned into training, development, and test sets for the competition.Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores.We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task.The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.
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Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646
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
Norms of valence, arousal, and dominance for 13,915 English lemmas
Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013) · Behavior Research Methods · doi:10.3758/s13428-012-0314-x · Cited by 1,959
Sentiment strength detection for the social web
Thelwall, M., Buckley, K., & Paltoglou, G. (2012) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21662 · Cited by 1,062
Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter
Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A., & Danforth, C. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026752 · Cited by 809
SemEval-2018 Task 1: Affect in Tweets
Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018) · Proceedings of The 12th International Workshop on Semantic Evaluation · doi:10.18653/v1/s18-1001 · Cited by 746
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
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SemEval-2018 Task 1: Affect in Tweets
Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018) · Proceedings of The 12th International Workshop on Semantic Evaluation · doi:10.18653/v1/s18-1001 · Cited by 746
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
Self-citationMohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391
Self-citationUnderstanding Emotions in Text Using Deep Learning and Big Data
Chatterjee, A., Gupta, U., Chinnakotla, M. K., Srikanth, R., Galley, M., & Agrawal, P. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.029 · Cited by 384
Text‐based emotion detection: Advances, challenges, and opportunities
Acheampong, F. A., Wenyu, C., & Nunoo‐Mensah, H. (2020) · Engineering Reports · doi:10.1002/eng2.12189 · Cited by 367
Metadata
- Authors
- Mohammad, S., & Bravo-Marquez, F.
- Venue
- Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Published
- 2017
- Pages
- 34-49
- OpenAlex
- W2747541555
- Cited by
- 270
- References
- 9
- FWCI
- 18.42
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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