ADVANCED SENTIMENTAL ANALYSIS USING ML AND NLP FOR TEXTUAL DATA INTERPRETATION

Abstract

In today’s data-driven world, the surge of user-generated textual content across digital platforms presents both a challenge and an opportunity for deriving actionable insights. This project introduces an advanced sentiment analysis system that leverages the combined power of Machine Learning (ML) and Natural Language Processing (NLP) to interpret and classify emotions within unstructured text data. Unlike conventional methods that rely on simple polarity detection, the proposed system incorporates deep learning models such as BERT, RoBERTa, and LSTM to understand linguistic context and complex emotional undertones. Advanced word embeddings like Word2Vec, GloVe, and FastText are utilized for effective feature representation, enabling the detection of nuanced sentiments including sarcasm, ambivalence, and mixed emotions. The system is developed using Python-based libraries like NLTK, SpaCy, TensorFlow, and PyTorch, integrated into a scalable architecture supported by Apache Spark and Hadoop. Real-time data ingestion from APIs such as Twitter and Reddit allows for continuous sentiment monitoring, while a user-friendly web dashboard built with Flask or FastAPI presents insights through sentiment timelines, emotion heatmaps, and word clouds. Key features include multi-language support, emotion-specific tagging, and sentiment filtering by demographics and time. This framework has vast applications in business intelligence, social analytics, market forecasting, and public opinion tracking.