A free, open source, self-hosted customer feedback tool 🦊
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Updated
Jan 9, 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.
Sample google appscripts
🏠 The source code of bimbala.com.
Free and unlimited feedback widget for your websites through Google Analytics
💬 It uses NLP techniques to classify reviews as positive, neutral or negative, providing valuable insights into customer feedback.
Customer Feedback and Sentiment Analysis
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..
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.
For feedback collection and external issue tracking
Emaily is a full-stack web application to collect customer feedback. It is for startups and product managers. It is written in JavaScript using Node.js, Express, Mongo DB, React, Redux, and Material UI.
Mechanism for a search engine backed by inverted indexes (from using Lucene and Hadoop). Word embeddings and Snippet generation were used.
A Rust crate for calculating Net Promoter Score (NPS) from survey responses.
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.
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