Sentiment analysis has emerged as a critical application of natural language processing (NLP) in the digita l age. This paper presents Sentiment Analyzer, a comprehensive multi-method sentiment analysis system that combines lexicon-based methods (VADER, TextBlob), machine learning (ML) classifiers, and ensemble techniques to provide accurate and robust sentiment detection. The system implements a modular architecture with components for text preprocessing, sentiment analysis, emotion detection, emoji analysis, and result visualization. A FastAPI-based REST API enables programmatic access, while an interactive Streamlit dashboard provides a user-friendly interface. The ML pipeline employs TF-IDF vectorization with Logistic Regression, Naive Bayes, and Support Vector Machine classifiers. Experimental evaluation on the SST-2 benchmark demonstrates ensemble classification accuracy of 91.3%, outperforming standalone VADER (71.3%) and basic Logistic Regression (81.2%). API endpoints respond in under 50 ms for single-text analysis, and batch processing of 100 texts completes in under 450 ms.