Advanced Sentiment Analysis using Neural Networks for Understanding Human Emotions
DOI:
https://doi.org/10.15662/IJEETR.2026.0802447Keywords:
Sentiment Analysis, Deep Learning, Neural Networks, LSTM, CNN, Emotion Detection, Natural Language ProcessingAbstract
Sentiment analysis is an important task in natural language processing that aims to identify human emotions from textual data such as emails, letters, and online messages. This paper presents an advanced deep learning–based sentiment analysis system to classify emotions including happiness, sadness, anger, and fear. The proposed approach employs neural network models such as Long Short-Term Memory (LSTM) and transformer-based architectures to capture contextual and semantic relationships in text. Preprocessing techniques are applied to enhance data quality and improve model performance. Experimental results demonstrate that the proposed system achieves higher accuracy and reliability compared to traditional machine learning methods. The system can be effectively applied in customer feedback analysis, human resource management, and mental health monitoring
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