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 modelin
Social media platforms make a significant contribution to modeling and influencing people’s opinions...
Classifying latent attributes of social media users has many applications in public health, politics...
This paper describes our approach to the SemEval 2016 task 4, “Sentiment Analysis in Twitter”, where...
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...
This paper deals with the quality of textual features in messages in order to classify tweets. The a...
Many online service systems leverage user-generated content from Web 2.0 style platforms such as Wik...
Elections unleash strong political views on Twitter, but what do peoplereally think about politics? ...
Abstract. Twitter conveys the opinions and interests of people in various topics and domains. In thi...
This paper addresses the task of user classification in social media, with an application to Twitter...
Scholars have access to a rich source of political discourse via social media. Although computationa...
Abstract. Opinion and trend mining on micro blogs like twitter re-cently attracted research interest...
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...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Social media platforms make a significant contribution to modeling and influencing people’s opinions...
Classifying latent attributes of social media users has many applications in public health, politics...
This paper describes our approach to the SemEval 2016 task 4, “Sentiment Analysis in Twitter”, where...
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...
This paper deals with the quality of textual features in messages in order to classify tweets. The a...
Many online service systems leverage user-generated content from Web 2.0 style platforms such as Wik...
Elections unleash strong political views on Twitter, but what do peoplereally think about politics? ...
Abstract. Twitter conveys the opinions and interests of people in various topics and domains. In thi...
This paper addresses the task of user classification in social media, with an application to Twitter...
Scholars have access to a rich source of political discourse via social media. Although computationa...
Abstract. Opinion and trend mining on micro blogs like twitter re-cently attracted research interest...
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...
Supervised machine learning methods are increasingly employed in political science. Such models requ...
Social media platforms make a significant contribution to modeling and influencing people’s opinions...
Classifying latent attributes of social media users has many applications in public health, politics...
This paper describes our approach to the SemEval 2016 task 4, “Sentiment Analysis in Twitter”, where...