This paper discusses the fourth year of the ”Sentiment Analysis in Twitter Task”. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic “sentiment classification in Twitter” task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams.
Описание
This paper details the SemEval-2016 task on Sentiment Analysis in Twitter, offering insights into the advancements and challenges in sentiment analysis of Twitter data.
%0 Generic
%1 Preslav2016
%A Nakov, Preslav
%A Ritter, Alan
%A Rosenthal, Sara
%A Sebastiani, F.
%A Stoyanov, Veselin
%D 2016
%K sentiment-analysis Twitter SemEval related_works machine-learning related_works_benchmark posted_with_chatgpt
%P 1-18
%R 10.18653/v1/S16-1001
%T SemEval-2016 Task 4: Sentiment Analysis in Twitter
%U https://www.semanticscholar.org/paper/846243cb26202b827d9926201ffaaa4cee65b5ae
%X This paper discusses the fourth year of the ”Sentiment Analysis in Twitter Task”. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic “sentiment classification in Twitter” task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams.
@JournalArticle{Preslav2016,
abstract = {This paper discusses the fourth year of the ”Sentiment Analysis in Twitter Task”. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new subtasks focus on two variants of the basic “sentiment classification in Twitter” task. The first variant adopts a five-point scale, which confers an ordinal character to the classification task. The second variant focuses on the correct estimation of the prevalence of each class of interest, a task which has been called quantification in the supervised learning literature. The task continues to be very popular, attracting a total of 43 teams.},
added-at = {2023-09-22T12:19:03.000+0200},
author = {Nakov, Preslav and Ritter, Alan and Rosenthal, Sara and Sebastiani, F. and Stoyanov, Veselin},
biburl = {https://www.bibsonomy.org/bibtex/226bec16efa1de4c013e6b0156d72e417/tomvoelker},
day = 1,
description = {This paper details the SemEval-2016 task on Sentiment Analysis in Twitter, offering insights into the advancements and challenges in sentiment analysis of Twitter data.},
doi = {10.18653/v1/S16-1001},
interhash = {4d2830e2291bd6e37b347fda3e9a48bc},
intrahash = {26bec16efa1de4c013e6b0156d72e417},
keywords = {sentiment-analysis Twitter SemEval related_works machine-learning related_works_benchmark posted_with_chatgpt},
month = {6},
pages = {1-18},
timestamp = {2023-09-22T12:19:03.000+0200},
title = {SemEval-2016 Task 4: Sentiment Analysis in Twitter},
url = {https://www.semanticscholar.org/paper/846243cb26202b827d9926201ffaaa4cee65b5ae},
year = 2016
}