GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus

2019/05/23 by Shabnam Tafreshi, Mona Diab, Tafreshi, Shabnam +1
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Sentiment Analysis and Opinion Mining #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1905.09439

openalex publication_date 2019/05/23 · openalex created_date 2019/05/29 · openalex updated_date 2026/07/28

Abstract

In this paper we present an emotion classifier model submitted to the SemEval-2019 Task 3: EmoContext. The task objective is to classify emotion (i.e. happy, sad, angry) in a 3-turn conversational data set. We formulate the task as a classification problem and introduce a Gated Recurrent Neural Network (GRU) model with attention layer, which is bootstrapped with contextual information and trained with a multigenre corpus. We utilize different word embeddings to empirically select the most suited one to represent our features. We train the model with a multigenre emotion corpus to leverage using all available training sets to bootstrap the results. We achieved overall %56.05 f1-score and placed 144.

Citations

• Adam: A Method for Stochastic Optimization
• Better Mini-Batch Algorithms via Accelerated Gradient Methods
• A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales

Related

• AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification
• ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN
• Transformer based neural networks for emotion recognition in conversations
• ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
• NUS-Emo at SemEval-2024 Task 3: Instruction-Tuning LLM for Multimodal Emotion-Cause Analysis in Conversations