由AI驱动的多场景通用评论内容深度分析工具，支持双模洞察系统(CLI本地模式+Gemini增强模式)，提供22维度智能标签、黑金奢华可视化看板和四位一体VOC系统 | AI-powered multi-scenario review analysis tool with dual-mode insight system (CLI + Gemini), 22-dimension tags, premium dashboard & 4-in-1 VOC system

This project performed sentimental analysis based on opinion words (like good, bad, beautiful, wrong, best, awesome, etc) of selected opinion target ( like product name for amazon product reviews).

Quy Nhon AI Hackathon 2022 - Challenge 2: Review Analytics - Top 1 Solution

LADy 💃: A Benchmark Toolkit for Latent Aspect Detection Enriched with Backtranslation Augmentation

Game Review Analysis in Steam for 2019 HYU Social Network Analysis and Text Mining Term Project

The Amaon Fine Foods Review dataset consists of reviews of fine foods from Amazon. There are approximate 500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. The Aim of this case study was to predict the polarity of the reviews ie. positive/negative. I have applied various Machine Learning Techniques & Algorithms and documented the results accordingly

This project uses Machine Learning, Natural Language Processing (NLP), and Web Scraping in order to get real customer reviews for any product on Amazon and perform sentiment analysis that predicts whether the reviews are positive or negative.

Sentique is a full‑stack feedback analytics platform that ingests user reviews from App Store, Google Play, Trustpilot, Reddit and Twitter/X, processes them with a fine‑tuned BERT model into 16 categories and sentiment labels, and provides comprehensive analytics and insights.

An AI-powered system for automated academic peer review. Upload a PDF, and the assistant analyzes novelty, plagiarism, factual accuracy, claim mapping, and citation quality (via GROBID). Includes an optional Deep Search mode to fetch and index new papers for comparison

Systematic Literature Review / Revisão Sistemática da Literatura

📊 A comprehensive Python toolkit that leverages local Large Language Models (LLMs) via Ollama to analyze Steam game reviews.

An AI project developed by interns Nehal, Huzzaifa, and Romman, under the mentorship of Firoz Shaikh, Head of AI Team at Nexgeno.

Framework for mining and analyzing issues from product reviews and interactive visualisation using plotly dash. Finalist project at Lauzhack 2023.

AI-powered tool that extracts pros, cons, summary, and sentiment from product reviews using the DeepSeek-Prover-V2-671B model via a Streamlit interface.

Opinion classification with kili-technology and huggingface by fine-tuning roBERTa model.

AI-powered sentiment analysis tool for evaluating processes and tracking feedback. Extract positive insights from reviews, customer feedback, and text data with real-time analysis and visualization.

Software that fits how you actually work. Makers of https://www.reviato.com

A Simple, Minimal Review Collector

An AI assistant that digs through Google Play and Apple App Store reviews, compares up to five apps side-by-side, and tells you what actually matters. It tells what users genuinely love, what frustrates them, and what they keep asking for—so PMs can move from scattered feedback to clear, actionable product insights.

This project analyzes 1,500 customer reviews from Booking.com for La Veranda Hotel (Larnaca, Cyprus) using Natural Language Processing (NLP). It performs sentiment scoring, topic modeling (LDA), and geographic sentiment analysis to uncover actionable insights that can improve hospitality operations and marketing strategy. Built using Orange.

Интеллектуальная система для обработки отзывов на базе GPT

Insight Platter: A comprehensive platform offering actionable insights from restaurant reviews through web scraping, sentiment analysis, and data visualization.

This project aims to analyze consumer sentiment towards (FMCG) company products by scraping reviews & performing text analysis using Python. By leveraging NLP techniques, such as sentiment analysis, word cloud and topic modelling. The results of this study can inform product development, marketing strategies & overall business decision-making

Zero-Noise utilities for safer product research and review signal analysis.

This repository contains the code for a rating review classification project that was submitted for the Kaggle Wars competition hosted by ACM Thapar. The project aims to classify reviews based on their rating, using data pre-processing and a convolutional neural network (CNN) model.

Analysing Amazon customer reviews via Clustering, Visualization and Classification

2021 Introduction-to-Information-Retrieval-and-Text-Mining Final Project

Antimicrobial Activity on Copper Study & Lab Work

Extract customer reviews from some online stores and classify negative reviews.

A Machine Learning model for sentiment analysis of Amazon reviews that can predict the customer sentiments based on their product reviews