Sentiment valence regression

Given a tweet, determine the intensity of sentiment or valence (V) 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
SemEval-2018 Task 1: Affect in Tweets
Language
Spanish
English
NLP topic
sentiment analysis
Abstract task
Regression
Dataset
Affect in Tweets-ES
Year
2018
Publication link
https://aclanthology.org/S18-…
Ranking metric
Pearson correlation

Task results

System Pearson correlation
Median Team 0.6090
ELiRF-UPV 0.7420
AffectThor 0.7950
Amobee 0.7700

If you have published a result better than those on the list, send a message to odesia-comunicacion@lsi.uned.es indicating the result and the DOI of the article, along with a copy of it if it is not published openly.