%0 Generic %1 Preslav2013 %A Nakov, Preslav %A Rosenthal, Sara %A Kozareva, Zornitsa %A Stoyanov, Veselin %A Ritter, Alan %A Wilson, Theresa %D 2013 %K sentiment-analysis Twitter SemEval related_works machine-learning related_works_benchmark posted_with_chatgpt %P 312-320 %T SemEval-2013 Task 2: Sentiment Analysis in Twitter %U https://www.semanticscholar.org/paper/ac5478ca3027903ef0c00429e6e6491a3e897006 %X In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the comparison between approaches. To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a messagelevel subtask. We used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks. All datasets used in the evaluation are released to the research community. The task attracted significant interest and a total of 149 submissions from 44 teams. The bestperforming team achieved an F1 of 88.9% and 69% for subtasks A and B, respectively.

@JournalArticle{Preslav2013, abstract = {In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the comparison between approaches. To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a messagelevel subtask. We used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks. All datasets used in the evaluation are released to the research community. The task attracted significant interest and a total of 149 submissions from 44 teams. The bestperforming team achieved an F1 of 88.9% and 69% for subtasks A and B, respectively.}, added-at = {2023-09-22T12:18:32.000+0200}, author = {Nakov, Preslav and Rosenthal, Sara and Kozareva, Zornitsa and Stoyanov, Veselin and Ritter, Alan and Wilson, Theresa}, biburl = {https://www.bibsonomy.org/bibtex/26b62cdf9ffa95c3d59525344781fd358/tomvoelker}, day = 1, description = {This paper presents the SemEval-2013 task on Sentiment Analysis in Twitter, focusing on the challenges of analyzing sentiments in short and informal Twitter messages.}, interhash = {f4d2b0eeb17a5fdfba16db7322628ceb}, intrahash = {6b62cdf9ffa95c3d59525344781fd358}, keywords = {sentiment-analysis Twitter SemEval related_works machine-learning related_works_benchmark posted_with_chatgpt}, month = {6}, pages = {312-320}, timestamp = {2023-09-22T12:18:32.000+0200}, title = {SemEval-2013 Task 2: Sentiment Analysis in Twitter}, url = {https://www.semanticscholar.org/paper/ac5478ca3027903ef0c00429e6e6491a3e897006}, year = 2013 }