%0 Generic %1 Preslav2014 %A Nakov, Preslav %A Ritter, Alan %A Rosenthal, Sara %A Sebastiani, F. %A Stoyanov, Veselin %D 2014 %K sentiment-analysis Twitter SemEval related_works machine-learning related_works_benchmark posted_with_chatgpt %P 73-80 %R 10.3115/v1/S14-2009 %T SemEval-2014 Task 9: Sentiment Analysis in Twitter %U https://www.semanticscholar.org/paper/2b74f47b1336c948d2371a30209ec56c936763a9 %X We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year’s task that ran successfully as part of SemEval2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) LiveJournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B.

@JournalArticle{Preslav2014, abstract = {We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year’s task that ran successfully as part of SemEval2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) LiveJournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B.}, added-at = {2023-09-22T12:19:36.000+0200}, author = {Nakov, Preslav and Ritter, Alan and Rosenthal, Sara and Sebastiani, F. and Stoyanov, Veselin}, biburl = {https://www.bibsonomy.org/bibtex/2554f11c9350be6235d3194b682383e2e/tomvoelker}, day = 1, description = {This paper outlines the SemEval-2014 task on Sentiment Analysis in Twitter, highlighting the methodologies and challenges in sentiment analysis of Twitter messages.}, doi = {10.3115/v1/S14-2009}, interhash = {274e6d0a609a6beb3a128e5bb54a9b74}, intrahash = {554f11c9350be6235d3194b682383e2e}, keywords = {sentiment-analysis Twitter SemEval related_works machine-learning related_works_benchmark posted_with_chatgpt}, month = {8}, pages = {73-80}, timestamp = {2023-09-22T12:19:36.000+0200}, title = {SemEval-2014 Task 9: Sentiment Analysis in Twitter}, url = {https://www.semanticscholar.org/paper/2b74f47b1336c948d2371a30209ec56c936763a9}, year = 2014 }