A free, open source, self-hosted customer feedback tool 🦊
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Updated
Apr 10, 2025 - TypeScript
A free, open source, self-hosted customer feedback tool 🦊
Track your customers feedback to build better products with LogChimp. ⭐️ Star to support our work!
Multi Class Text (Feedback) Classification using CNN, GRU Network and pre trained Word2Vec embedding, word embeddings on TensorFlow.
Using Machine Learning to Analyze & Visualize Consumer Behavior
A customer feedback demo application for collecting reviews for a product after a successful purchase.
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.
Sample google appscripts
Free and unlimited feedback widget for your websites through Google Analytics
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..
A short hand-picked collection of resources to help SaaS founders get started with customer interviews.
💬 It uses NLP techniques to classify reviews as positive, neutral or negative, providing valuable insights into customer feedback.
Customer Feedback and Sentiment Analysis
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.
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..
An Analysis of the tech review and customer feedback on twitter, provided an insight into what the customers seek from Apple’s iPhone Series.
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.
As part of our commitment to transparency and innovation in legal technology, Express Legal Funding is proud to release our customer reviews dataset as an open resource for researchers, developers, and AI model trainers.
Mechanism for a search engine backed by inverted indexes (from using Lucene and Hadoop). Word embeddings and Snippet generation were used.
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