%0 Generic %1 Sara2015 %A Rosenthal, Sara %A Nakov, Preslav %A Kiritchenko, Svetlana %A Mohammad, Saif M. %A Ritter, Alan %A Stoyanov, Veselin %D 2015 %J ArXiv %K sentiment-analysis Twitter SemEval related_works machine-learning related_works_benchmark posted_with_chatgpt %R 10.18653/v1/S15-2078 %T SemEval-2015 Task 10: Sentiment Analysis in Twitter %U https://www.semanticscholar.org/paper/ae47d53a9d712d655f88362bddfae226c651742a %V abs/1912.02387 %X In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year’s shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phrase in the context of a tweet, and (B) overall sentiment of a tweet. We further included three new subtasks asking to predict (C) the sentiment towards a topic in a single tweet, (D) the overall sentiment towards a topic in a set of tweets, and (E) the degree of prior polarity of a phrase.

@JournalArticle{Sara2015, abstract = {In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year’s shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phrase in the context of a tweet, and (B) overall sentiment of a tweet. We further included three new subtasks asking to predict (C) the sentiment towards a topic in a single tweet, (D) the overall sentiment towards a topic in a set of tweets, and (E) the degree of prior polarity of a phrase.}, added-at = {2023-09-22T12:20:07.000+0200}, author = {Rosenthal, Sara and Nakov, Preslav and Kiritchenko, Svetlana and Mohammad, Saif M. and Ritter, Alan and Stoyanov, Veselin}, biburl = {https://www.bibsonomy.org/bibtex/2723a2efb74bff24be5d14d61ed167bec/tomvoelker}, day = 1, description = {The paper describes the SemEval-2015 task on Sentiment Analysis in Twitter, discussing the approaches and challenges in analyzing sentiments in Twitter posts.}, doi = {10.18653/v1/S15-2078}, interhash = {11fad04055c3904b96f4dedbae72b285}, intrahash = {723a2efb74bff24be5d14d61ed167bec}, journal = {ArXiv}, keywords = {sentiment-analysis Twitter SemEval related_works machine-learning related_works_benchmark posted_with_chatgpt}, month = {6}, timestamp = {2023-09-22T12:20:07.000+0200}, title = {SemEval-2015 Task 10: Sentiment Analysis in Twitter}, url = {https://www.semanticscholar.org/paper/ae47d53a9d712d655f88362bddfae226c651742a}, volume = {abs/1912.02387}, year = 2015 }