%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 }