SemEval-2017 Task 4: Sentiment Analysis in Twitter

Sara Rosenthal; Noura Farra; Preslav Nakov

SemEval-2017 Task 4: Sentiment Analysis in Twitter

Computation and Language 2019-12-03 v1 Information Retrieval Machine Learning

Authors: Sara Rosenthal , Noura Farra , Preslav Nakov

Abstract

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 two-point 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.

Keywords

shared task evaluation sentiment analysis social media analysis

Cite

@article{arxiv.1912.00741,
  title  = {SemEval-2017 Task 4: Sentiment Analysis in Twitter},
  author = {Sara Rosenthal and Noura Farra and Preslav Nakov},
  journal= {arXiv preprint arXiv:1912.00741},
  year   = {2019}
}

Comments

sentiment analysis, Twitter, classification, quantification, ranking, English, Arabic

SemEval-2017 Task 4: Sentiment Analysis in Twitter

Abstract

Keywords

Cite

Comments

Related papers