The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
SemEval-2017 Task 4: Sentiment Analysis in Twitter
Open accessAbstract
This paper describes the fifth year of the Sentiment Analysis in Twitter task.SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a twopoint and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale.Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets.The task continues to be very popular, with a total of 48 teams participating this year.
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Cites
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. J. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 4,568
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14031 · Cited by 1,923
SemEval-2014 Task 4: Aspect Based Sentiment Analysis
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., & Manandhar, S. (2014) · Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) · doi:10.3115/v1/s14-2004 · Cited by 1,862
SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., et al. (2016) · Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) · doi:10.18653/v1/s16-1002 · Cited by 1,281
SemEval-2015 Task 12: Aspect Based Sentiment Analysis
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., & Androutsopoulos, I. (2015) · Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) · doi:10.18653/v1/s15-2082 · Cited by 1,149
SemEval-2016 Task 4: Sentiment Analysis in Twitter
Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., & Stoyanov, V. (2016) · Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) · doi:10.18653/v1/s16-1001 · Cited by 966
Self-citationSemEval-2016 Task 6: Detecting Stance in Tweets
Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., & Cherry, C. (2016) · Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) · doi:10.18653/v1/s16-1003 · Cited by 844
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
Understanding the Demographics of Twitter Users
Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., & Rosenquist, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14168 · Cited by 759
Cited by
van Atteveldt, W., van der Velden, M. A. C. G., & Boukes, M. (2021) · Communication Methods and Measures · doi:10.1080/19312458.2020.1869198 · Cited by 409
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
CARER: Contextualized Affect Representations for Emotion Recognition
Saravia, E., Liu, H.-C. T., Huang, Y.-H., Wu, J., & Chen, Y.-S. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1404 · Cited by 308
A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research
Tan, K. L., Lee, C. P., & Lim, K. M. (2023) · Applied Sciences · doi:10.3390/app13074550 · Cited by 303
SemEval-2018 Task 3: Irony Detection in English Tweets
Van Hee, C., Lefever, E., & Hoste, V. (2018) · Proceedings of The 12th International Workshop on Semantic Evaluation · doi:10.18653/v1/s18-1005 · Cited by 292
Metadata
- Authors
- Rosenthal, S., Farra, N., & Nakov, P.
- Venue
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Published
- 2017
- Pages
- 502-518
- OpenAlex
- W2916132663
- Cited by
- 870
- References
- 57
- FWCI
- 52.34
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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