2014 · 618 citations · 20 references
ComputingComputer ScienceComputational LinguisticsNatural Language ProcessingText AnalyticsCorpus AnalysisOpinion MiningSentiment AnalysisSocial MediaSentiment ClassificationSentiment FeaturesSocial Media SentimentTextual Sentiment AnalysisTrec Evaluation FrameworkTweet Sentiment Analysis
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 SemEval-2013. 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.
20
Bo Pang, Lillian Lee, Shivakumar Vaithyanathan · 2002 · 7K citations · Full text
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Andranik Tumasjan, Timm O. Sprenger, Philipp Sandner et al. · Proceedings of the International AAAI Conference on Web and Social Media · 2010 · 2.7K citations · Full text
Social Sciences, Political Communication, Political Science +12
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
Brendan O’Connor, Ramnath Balasubramanyan, Bryan Routledge et al. · Proceedings of the International AAAI Conference on Web and Social Media · 2010 · 2K citations · Full text
Public Affairs, Political Communication, Political Science +17