A free, open source, self-hosted customer feedback tool 🦊

🔥 🔥 🔥 Open Source Canny, ProductBoard, UserJot Alternative. Track your customers feedback to build better products with LogChimp. ⭐️ Star to support our work!

A lightweight, self-hostable changelog and roadmap web application that lets you share product updates with your community and gather feedback through voting on upcoming features. Built with SvelteKit and Go.

A short hand-picked collection of resources to help SaaS founders get started with customer interviews.

Multi Class Text (Feedback) Classification using CNN, GRU Network and pre trained Word2Vec embedding, word embeddings on TensorFlow.

MCP server that triangulates customer support tickets and feature requests to help PMs decide what to build next

The open source alternative to Canny, UserVoice, Productboard

Using Machine Learning to Analyze & Visualize Consumer Behavior

Canny MCP Server for Canny API integration with AI Assistants. Features Jira linking, batch operations, ETA management, company MRR tracking, and PM-focused workflows.

A customer feedback demo application for collecting reviews for a product after a successful purchase.

Sample google appscripts

B2B Customer Feedback collection and analysis SaaS using AI/ML to generate, cluster and rank actionable tasks, helping you to grow your business.

🏠 The source code of bimbala.com.

Twitter/X comment and reply scraper with sentiment analysis

Advanced Turkish NLP pipeline using BERT for sentiment analysis and topic extraction. Features dual-method sentiment analysis, semantic clustering, and ChatGPT API integration for comprehensive Turkish text analytics.

This repository contains code and tools for sentiment analysis of Persian customer reviews and feedback. Using Natural Language Processing (NLP) techniques, this project helps you transform Persian customer reviews into interpretable data and gain valuable insights to enhance the shopping experience.

Modern hotel review sentiment analysis with interactive GUI, AI explanations, and educational features. Python/ML/NLP project.

voiceform‑fill turns spoken answers into structured form text using advanced models, enabling hands‑free, accurate data entry.

The cab booking project is used to book online from where you need there are three user admin and user and cab driver, The admin can check the cab details who all booked who all login etc, I have provide user-friendly domain to customer's happy it will helpful to all users used my cab booking online..

A simple and interactive visualization tool built with Python and Streamlit to analyze customer reviews of airlines. The tool performs sentiment analysis (Positive/Negative/Neutral) on textual reviews and provides interactive charts and rankings for quick insights.

Automated Restaurant Feedback Agent – SteamNoodles🍜 (AgentX Mini Project)

Customer-Review-Feedback

Developed a full-stack web application for catering services using the MERN stack (MongoDB, Express.js, React, Node.js). Features include menu management, order tracking, customer feedback, payment gateway and many more..

This project analyzes customer feedback for skincare products by predicting sentiment using an unsupervised model. It includes a web application for real-time sentiment analysis, an ETL pipeline built with Azure Data Factory, Azure Databricks, and Azure Synapse Analytics, and a Power BI dashboard for visualizing review trends.

Batch-optimized LLM-based automated customer review classification and sentiment detection engine using Ollama + Llama3.

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.

Customer Feedback and Sentiment Analysis

💬 It uses NLP techniques to classify reviews as positive, neutral or negative, providing valuable insights into customer feedback.

A modern, production-ready feedback collection widget system built with Next.js, React, TypeScript, Prisma, and MongoDB. This project provides an embeddable web component that can be integrated into any website, along with a comprehensive dashboard for managing projects, viewing feedback, and analyzing insights.

This project automates the analysis of large-scale customer feedback using Natural Language Processing (NLP) and Machine Learning. The core of this tool is a model that instantly translates raw review text into a corresponding star rating, providing rapid and actionable insights into product satisfaction.