psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis
In this paper, we describe the first attempt to perform transfer learning from sentiment to emotions. Our system employs Long Short-Term Memory (LSTM) networks, including bidirectional LSTM (biLSTM) and LSTM with attention mechanism. We perform transfer learning by first pre-training the LSTM networks on sentiment data before concatenating the penultimate layers of these networks into a single vector as input to new dense layers. For the E-c subtask, we utilize a novel approach to train models for correlated emotion classes. Our system performs 4/48, 3/39, 8/38, 4/37, 4/35 on all English subtasks EI-reg, EI-oc, V-reg, V-oc, E-c of SemEval 2018 Task 1: Affect in Tweets.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion ClassificationEmotion RecognitionGeneral ClassificationSentiment AnalysisTransfer LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction
This paper describes a system attended in the SemEval-2018 Task 1 {``}Affect in tweets{''} that predicts emotional intensities. We use Group LSTM with an attention model and transfer learning with sentiment classificatio…
General ClassificationregressionSentiment AnalysisSentiment Classification+2Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis
In this paper, we propose a two-layered multi-task attention based neural network that performs sentiment analysis through emotion analysis. The proposed approach is based on Bidirectional Long Short-Term Memory and uses…
Emotion RecognitionSentiment AnalysisISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets
This paper presents a UIR-Miner system for emotion and sentiment analysis evaluation in Twitter in SemEval 2018. Our system consists of three main modules: preprocessing module, stacking module to solve the intensity pre…
ClassificationEmotion ClassificationGeneral ClassificationMulti-Label Classification+3SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection
The system developed by the SSN MLRG1 team for Semeval-2018 task 1 on affect in tweets uses rule based feature selection and one-hot encoding to generate the input feature vector. Multilayer Perceptron was used to build …
Classificationfeature selectionGeneral ClassificationOrdinal Classification+2ConSSED at SemEval-2019 Task 3: Configurable Semantic and Sentiment Emotion Detector
This paper describes our system participating in the SemEval-2019 Task 3: EmoContext: Contextual Emotion Detection in Text. The goal was to for a given textual dialogue, i.e. a user utterance along with two turns of cont…
Emotion Recognition in ConversationGeneral Classification