Deep Learning-Driven Sentiment Analysis in Textual Data

Emotional recognition from text is essential in natural language processing with far-reaching consequences in areas such as Artificial Intelligence, Human-Computer Interaction, and others. Emotions are felt, thought-out physical responses to events. Analyzing these feelings independently of vocal and facial cues is critical to interpreting emotions. Despite these challenges, it is crucial to understand human emotions, especially as people become more comfortable expressing themselves through hate speech on sites like Facebook, Twitter, etc. This paper discusses how to classify many tweets based on their tone. Here, we use deep learning algorithms to determine whether an expression means a happy or sad emotion. There are four distinct negative states of mind: rage, indifference, loneliness, disdain, sadness, and despair. The subset of positive emotions includes zeal, joy, happiness, love, calm, pleasure, and wonder. Using three datasets, we validated and assessed the method's use of long short-term memory and recurrent neural networks to obtain high accuracy in emotion categorization. With an 85.07% predictability for positive and negative classification and an 87.23% and 86.3% accuracy for positive and negative subclasses, respectively, a detailed examination reveals that the system improves emotion prediction using the LSTM model.