EmoNet Deep Learning for Emotion Classification in Social Media Content Analysis

Recent developments in emotion classification for social media content evaluation have exposed the limitations of traditional machine learning (ML) algorithms, which struggle to represent the complex complexity of human emotions in text. In response, EmoNet, a novel deep learning technique, has been developed to deal with these problematic circumstances effectively. EmoNet combines convolutional neural network (CNN) and bidirectional long short-time period memory (Bi-LSTM) layers to extract hierarchical representations from text, enabling it to detect complex emotional indicators in social media communications. EmoNet adapts more easily to varied datasets and language patterns seen in social media since it does not require guide function engineering. EmoNet consistently outperforms existing systems in accuracy, precision, and recall across numerous emotion categories, such as happiness, sadness, anger, surprise, and disgust, as evidenced by detailed studies of benchmark datasets. For example, EmoNet achieved an accuracy of 0.85, precision of 0.87, recall of 0.84, and F1 Score of 0.85, greatly exceeding existing systems. These effects demonstrate EmoNet’s high functionality in knowledge and emotion classification, making it an essential tool for sentiment evaluation and user behaviormodeling in online platforms.