Decoding Digital Emotions: Navigating Abusive Comments with Ethical and Legal Safeguards

This study presents a vigorous sentiment analysis framework to classify clients' comments into positive, neutral, or negative sentiments, pointed at handling the challenges of linguistic complexity, information imbalance, and client protection. The investigation coordinates profound learning models such as LSTM, CNN, and BERT. Text comments are processed utilizing tokenization, lemmatization, and vectorization, whereas audio comments are changed over into spectrograms for investigation utilizing CNNs. The system ensures scalability and real-time processing through structured databases and API integration, adhering to data privacy regulations like GDPR and CCPA. The discoveries highlight the model's high accuracy in opinion classification, its adaptability to advancing language trends, and its adequacy in identifying offensive content. This research offers a scalable and ethical solution to real-time sentiment analysis, enhancing user safety and informed decision-making. The framework has broad applications, observing open estimation and fostering safer digital interactions on social platforms.