IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets
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SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets
The paper describes the best performing system for the SemEval-2018 Affect in Tweets (English) sub-tasks. The system focuses on the ordinal classification and regression sub-tasks for valence and emotion. For ordinal cla…
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This paper describes participation in DeftEval 2020 (part of SemEval sharing task competition), and is focused on the sentence classification. Our approach to the task was to create an ensemble of several RNNs combined w…
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Propaganda spreads the ideology and beliefs of like-minded people, brainwashing their audiences, and sometimes leading to violence. SemEval 2020 Task-11 aims to design automated systems for news propaganda detection. Tas…
ArticlesEnsemble LearningPropaganda detectionTask 2TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier
This paper describes the TWINA system, with which we participated in SemEval-2017 Task 4B (Topic Based Message Polarity Classification {--} Two point scale) and 4D (two-point scale Tweet quantification). We implemented e…
ClassificationGeneral ClassificationInformation RetrievalPosition+2LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification
This paper describes the system developed at LIA for the SemEval-2017 evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in tweets. The system is an ensemble of Deep Neural Network (DNN) models:…
General ClassificationSentence ClassificationSentiment AnalysisSentiment Classification+1