Add to ORKGWe examine methods for improving models for automatically labeling social media data. In particular we evaluate active learning: a method for selecting candidate training data whose labeling the classification model would benefit most of. We show that this approach requires careful ex-periment design, when it is combined with language modeling <br/
Social media data enables political scientists to observe phenomena that have been otherwise difficu...
Understanding how political attention is divided and over what subjects is crucial for research on a...
Social scientists often classify text documents to use the resulting labels as an outcome or a predi...
We examine methods for improving models for automatically labeling social media data. In particular ...
We study the discursive practices of politicians and journalists on social media. For this we need m...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Elections unleash strong political views on Twitter, but what do peoplereally think about politics? ...
The task of classifying political tweets has been shown to be very difficult, with controversial res...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
This paper describes our approach to the SemEval 2016 task 4, “Sentiment Analysis in Twitter”, where...
This paper deals with the quality of textual features in messages in order to classify tweets. The a...
Abstract. Opinion and trend mining on micro blogs like twitter re-cently attracted research interest...
Many online service systems leverage user-generated content from Web 2.0 style platforms such as Wik...
This research, presented at the Oklahoma State University Undergraduate Summer Student Research Expo...
Abstract. Opinion mining on Twitter recently attracted research interest in politics using Informati...
Social media data enables political scientists to observe phenomena that have been otherwise difficu...
Understanding how political attention is divided and over what subjects is crucial for research on a...
Social scientists often classify text documents to use the resulting labels as an outcome or a predi...
We examine methods for improving models for automatically labeling social media data. In particular ...
We study the discursive practices of politicians and journalists on social media. For this we need m...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Elections unleash strong political views on Twitter, but what do peoplereally think about politics? ...
The task of classifying political tweets has been shown to be very difficult, with controversial res...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
This paper describes our approach to the SemEval 2016 task 4, “Sentiment Analysis in Twitter”, where...
This paper deals with the quality of textual features in messages in order to classify tweets. The a...
Abstract. Opinion and trend mining on micro blogs like twitter re-cently attracted research interest...
Many online service systems leverage user-generated content from Web 2.0 style platforms such as Wik...
This research, presented at the Oklahoma State University Undergraduate Summer Student Research Expo...
Abstract. Opinion mining on Twitter recently attracted research interest in politics using Informati...
Social media data enables political scientists to observe phenomena that have been otherwise difficu...
Understanding how political attention is divided and over what subjects is crucial for research on a...
Social scientists often classify text documents to use the resulting labels as an outcome or a predi...