SemEval-2017 Task 4: Sentiment Analysis in Twitter
S. Rosenthal, N. Farra, and P. Nakov. (2019)cite arxiv:1912.00741Comment: sentiment analysis, Twitter, classification, quantification, ranking, English, Arabic.
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.
Description
SemEval-2017 Task 4: Sentiment Analysis in Twitter
%0 Generic
%1 rosenthal2019semeval2017
%A Rosenthal, Sara
%A Farra, Noura
%A Nakov, Preslav
%D 2019
%K semeval sentiment sentimentanalysis twitter
%T SemEval-2017 Task 4: Sentiment Analysis in Twitter
%U http://arxiv.org/abs/1912.00741
%X 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.
@misc{rosenthal2019semeval2017,
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.},
added-at = {2020-03-03T17:30:04.000+0100},
author = {Rosenthal, Sara and Farra, Noura and Nakov, Preslav},
biburl = {https://www.bibsonomy.org/bibtex/287b82d54dc8dfc8e8a70c8068506409b/albinzehe},
description = {SemEval-2017 Task 4: Sentiment Analysis in Twitter},
interhash = {e330939662fbb94649915fb58b5e67fc},
intrahash = {87b82d54dc8dfc8e8a70c8068506409b},
keywords = {semeval sentiment sentimentanalysis twitter},
note = {cite arxiv:1912.00741Comment: sentiment analysis, Twitter, classification, quantification, ranking, English, Arabic},
timestamp = {2020-03-03T17:30:04.000+0100},
title = {SemEval-2017 Task 4: Sentiment Analysis in Twitter},
url = {http://arxiv.org/abs/1912.00741},
year = 2019
}