Treffer: Hybrid Machine Learning Approaches for Enhanced Sentiment Analysis.

This paper presents a hybrid sentiment analysis system that integrates lexicon-based, traditional machine learning, and deep learning techniques to classify textual data into positive, negative, or neutral sentiments. The system leverages Python-based libraries such as Scikit-learn, NLTK, and Transformers to preprocess text, extract features, and apply models including Support Vector Machine (SVM), Bidirectional Encoder Representations from Transformers (BERT), Linear Regression, and Valence Aware Dictionary and sEntiment Reasoner (VADER). The framework processes customer reviews from e-commerce platforms (Amazon, Flipkart) and social media (Instagram) using web scraping and provides actionable insights through sentiment summarization and visualizations (bar and pie charts). Experimental results demonstrate BERT's superior performance with 92.3% accuracy, followed by SVM (85.6%), Linear Regression (81.2%), and VADER (76.8%). The system addresses challenges like sarcasm, class imbalance, and scalability, offering a scalable, user-friendly solution for real-world applications in ecommerce, social media analytics, and brand reputation management. [ABSTRACT FROM AUTHOR]

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