Sentiment Analyzer – Amazon Review Sentiment Analysis using Machine Learning Classification Models

Описание: Sentiment analysis plays a critical role in interpreting large-scale public opinion data generated through online platforms. E-commerce environments, which host extensive volumes of customer reviews, provide valuable insights into consumer preferences and purchasing behavior. While prior research has explored aspect-based sentiment analysis using supervised machine learning models and feature engineering techniques such as TF-IDF and n-grams, many implementations remain confined to experimental evaluations without structured mechanisms for real-time interaction or iterative model enhancement.

This project introduces the User-Centric Iterative Refinement for Sentiment Classification (UIR-SC) Framework, developed as a full-stack web application using Python and the Django framework in accordance with IEEE academic standards. The framework utilizes natural language processing techniques to preprocess and vectorize textual data, followed by training multiple supervised machine learning models including K-Nearest Neighbors, AdaBoost, XGBoost, and Logistic Regression classifiers.

The highest-performing model is deployed within an interactive web interface, enabling users to input individual reviews and receive instant sentiment predictions. The system incorporates a structured feedback mechanism that allows users to validate or contest predictions. Administrators analyze collected feedback to refine model parameters and enhance classification accuracy over time.

Designed strictly for academic and research purposes, this project demonstrates applied sentiment analytics, supervised machine learning evaluation, and full-stack web deployment for scalable opinion mining and continuous model improvement systems.

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ieeeprojects, pythonprojects, djangoprojects, pythonwebapplications, pythonfullstackprojects, computerscienceprojects, computersciencefinalyearprojects, cseprojects, itprojects, finalyearprojects, finalsemprojects, finalyearstudentsprojects, btechprojects, beprojects, mtechprojects, meprojects, mcaprojects, mscprojects, majorprojects, miniprojects, liveprojects, researchorientedprojects

CATEGORY:
Education

AUDIENCE:
B.E, B.Tech, MCA, MSc, M.E, M.Tech, BCA and BSc – Universities in India & Abroad

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DISCLAIMER:
This project is developed strictly for academic and research purposes following IEEE guidelines.