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SemEval-2016 Task 4: Sentiment Analysis in Twitter
Open accessAbstract
This paper discusses the fourth year of the "Sentiment Analysis in Twitter Task".SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions.The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet.The three new subtasks focus on two variants of the basic "sentiment classification in Twitter" task.The first variant adopts a five-point scale, which confers an ordinal character to the classification task.The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature.The task continues to be very popular, attracting a total of 43 teams.
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Cites
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
Cited by
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1146 · Cited by 1,181
Sentiment analysis using deep learning architectures: a review
Yadav, A., & Vishwakarma, D. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09794-5 · Cited by 841
A survey of multimodal sentiment analysis
Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S.-F., & Pantic, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.08.003 · Cited by 601
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
Ribeiro, F. N., Araújo, M., Gonçalves, P., André Gonçalves, M., & Benevenuto, F. (2016) · EPJ Data Science · doi:10.1140/epjds/s13688-016-0085-1 · Cited by 468
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
Mohammad, S. M., Kiritchenko, S., & Zhu, X. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.6242 · Cited by 451
Metadata
- Authors
- Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., & Stoyanov, V.
- Venue
- Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
- Published
- 2016
- Pages
- 1-18
- OpenAlex
- W2460474657
- Cited by
- 966
- References
- 1
- FWCI
- 83.86
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., & Stoyanov, V. (2016). SemEval-2016 Task 4: Sentiment Analysis in Twitter. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). https://doi.org/10.18653/v1/s16-1001
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