Emotion Detection in Twitter Messages Using Combination of Long Short-Term Memory and Convolutional Deep Neural Networks

2025-03-26

AI Summary

This study addresses the challenge of fine-grained emotion recognition in social media, specifically for classifying Twitter posts into four discrete emotion categories: joy, anger, sadness, and fear. We propose a hybrid deep neural network that jointly leverages bidirectional Long Short-Term Memory (Bi-LSTM) networks to capture long-range contextual dependencies and Convolutional Neural Networks (CNNs) to extract local semantic features; the architecture integrates pretrained word embeddings within an end-to-end supervised learning framework. Our key contribution is the first application of a Bi-LSTM–CNN fusion architecture to fine-grained Twitter emotion classification. Extensive experiments on a multi-topic tweet dataset demonstrate that the model achieves a mean accuracy of 93%, outperforming state-of-the-art baselines by a statistically significant margin—thereby validating its effectiveness and cross-topic generalizability.

Technology Category

• Natural Language Processing: Text Classification & Sentiment Analysis
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Problem

• Detect emotions in Twitter messages using deep learning
• Classify user sentiments into four emotional categories
• Improve accuracy with LSTM and CNN hybrid model

Innovation

• Combines LSTM and CNN for emotion detection
• Uses supervised deep neural network learning
• Achieves 93% accuracy in Twitter analysis