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

## TL;DR

This paper presents a neural network model with attention for emotion detection in conversational data, leveraging multigenre training data and contextual information to improve classification accuracy.

## Contribution

Introduces a GRU-based model with attention that effectively incorporates contextual information and multigenre data for emotion classification in sentences.

## Key findings

- Achieved 56.05% F1-score on the EmoContext dataset.
- Demonstrated the effectiveness of contextual information in emotion detection.
- Selected optimal word embeddings empirically for feature representation.

## 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.