A Simple Attention-Based Mechanism for Bimodal Emotion Classification

TL;DR

This paper introduces a novel bimodal deep learning architecture with attention mechanisms that combines text and speech data to improve emotion classification accuracy, outperforming existing systems.

Contribution

The paper presents a new attention-based bimodal deep learning architecture trained on text and speech data for emotion classification, demonstrating superior performance.

Findings

Bimodal architectures outperform unimodal ones in emotion classification.

Attention mechanisms enhance model performance.

Proposed architecture surpasses several state-of-the-art systems.

Abstract

Big data contain rich information for machine learning algorithms to utilize when learning important features during classification tasks. Human beings express their emotion using certain words, speech (tone, pitch, speed) or facial expression. Artificial Intelligence approach to emotion classification are largely based on learning from textual information. However, public datasets containing text and speech data provide sufficient resources to train machine learning algorithms for the tack of emotion classification. In this paper, we present novel bimodal deep learning-based architectures enhanced with attention mechanism trained and tested on text and speech data for emotion classification. We report details of different deep learning based architectures and show the performance of each architecture including rigorous error analyses. Our finding suggests that deep learning based architectures trained on different types of data (text and speech) outperform architectures trained only on text or speech. Our proposed attention-based bimodal architecture outperforms several state-of-the-art systems in emotion classification.