This project entails sentiment analysis and argumentation mining into the recently published UN security council speeches (UNSC) corpus. The UNSC corpus contains ~65,000 UN security council speeches from ~5,000 security council meetings from years 1995-2017. Each meeting is split up into the various speeches given by member countries. Furthermore, speeches are annotated with dates, topics and overall meeting outcomes. The UNSC corpus is, however, not annotated for argumentation structures and sentiment polarities. In this project, we attempt to produce automatic machine-driven sentiment and argumentation annotations for the UNSC corpus; which could aid future human-driven annotations
This paper proposes a scheme for sentiment annotation. We show how the task can be made tractable by...
We present a corpus comprising the first general election debate between Clinton and Trump (17,190 w...
We present the second iteration of IGGSA’s Shared Task on Sentiment Analysis for German. It resumes ...
This project entails sentiment analysis and argumentation mining into the recently published UN secu...
This is a dataset of UN Security Council debates between January 1995 and December 2017. The officia...
The dataset consists of mid-length sentences from the Bosnian, Croatian and Serbian parliamentary pr...
We present a corpus of political debates annotated with aspect-based sentiment and a corpus analysis...
Accurate opinion mining requires the exact identification of the source and target of an opinion. To...
The UKP Sentential Argument Mining Corpus includes 25,492 sentences over eight controversial topics....
The Shared Task on Source and Target Extraction from Political Speeches (STEPS) first ran in 2014 an...
This paper describes an annotation scheme for argumentation in opinionated texts such as newspaper e...
This dataset is an extension of the original UKP Sentential Argument Mining Corpus which includes 25...
Decision making in social communities, such as families, companies, or parties, builds on debates an...
Abstract In this paper the authors seek to establish the most appro-priate mechanism for conducting ...
A corpus of Hansard UK Parliament Debates for use in the evaluation of sentiment analysis systems. T...
This paper proposes a scheme for sentiment annotation. We show how the task can be made tractable by...
We present a corpus comprising the first general election debate between Clinton and Trump (17,190 w...
We present the second iteration of IGGSA’s Shared Task on Sentiment Analysis for German. It resumes ...
This project entails sentiment analysis and argumentation mining into the recently published UN secu...
This is a dataset of UN Security Council debates between January 1995 and December 2017. The officia...
The dataset consists of mid-length sentences from the Bosnian, Croatian and Serbian parliamentary pr...
We present a corpus of political debates annotated with aspect-based sentiment and a corpus analysis...
Accurate opinion mining requires the exact identification of the source and target of an opinion. To...
The UKP Sentential Argument Mining Corpus includes 25,492 sentences over eight controversial topics....
The Shared Task on Source and Target Extraction from Political Speeches (STEPS) first ran in 2014 an...
This paper describes an annotation scheme for argumentation in opinionated texts such as newspaper e...
This dataset is an extension of the original UKP Sentential Argument Mining Corpus which includes 25...
Decision making in social communities, such as families, companies, or parties, builds on debates an...
Abstract In this paper the authors seek to establish the most appro-priate mechanism for conducting ...
A corpus of Hansard UK Parliament Debates for use in the evaluation of sentiment analysis systems. T...
This paper proposes a scheme for sentiment annotation. We show how the task can be made tractable by...
We present a corpus comprising the first general election debate between Clinton and Trump (17,190 w...
We present the second iteration of IGGSA’s Shared Task on Sentiment Analysis for German. It resumes ...