Given a tweet, classify it into one of seven ordinal classes, corresponding to various levels of positive and negative sentiment intensity, that best represents the mental state of the tweeter.
Publication
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, Svetlana Kiritchenko (2018) SemEval-2018 Task 1: Affect in Tweets. Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 1–17. New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics
Competition
Language
Spanish
English
NLP topic
Abstract task
Dataset
Year
2018
Publication link
Ranking metric
Pearson correlation
Task results
| System | Precision | Recall | F1 | CEM | Accuracy | MacroPrecision | MacroRecall | MacroF1 | RMSE | MicroPrecision | MicroRecall | MicroF1 | MAE | MAP | UAS | LAS | MLAS | BLEX | Pearson correlation Sort ascending | Spearman correlation | MeasureC | BERTScore | EMR | Exact Match | F0.5 | Hierarchical F | ICM | MeasureC | Propensity F | Reliability | Sensitivity | Sentiment Graph F1 | WAC | b2 | erde30 | sent | weighted f1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Amobee | 0.7650 | ||||||||||||||||||||||||||||||||||||
| AffecThor | 0.7560 | ||||||||||||||||||||||||||||||||||||
| ELiRF-UPV | 0.7290 | ||||||||||||||||||||||||||||||||||||
| Median Team | 0.5560 |
