This Node.js app built with MongoDB allows users to compare both scores from Yelp and Google+ for a restaurant at the same time. It uses the Yelp, Google Maps and Alchemy Labs APIs, and dynamically scrapes text content of reviews to be scored with sentiment analysis to generate a third score. While the data pipeline for the third score is built, a dynamic third score of each location is not currently available because of API rate limitations.

🧠 NLP pipeline on 10K+ Yelp reviews | TF-IDF | Sentiment Analysis | Streamlit Dashboard | Python

A scalable pipeline for automated extraction, preprocessing, and sentiment analysis of Yelp reviews. Uses advanced HTTP requests, HTML parsing, and text normalization (tokenization, stopword removal, lemmatization) to enable precise polarity and subjectivity analysis for consumer insights and business analytics.

Big data restaurant analytics pipeline on AWS — PySpark on EMR, Spark MLlib ALS recommender, scikit-learn sentiment analysis, and a Streamlit + Folium heatmap dashboard. Built on 7M+ Yelp reviews with S3, RDS, and EMR.

End-to-end cloud data pipeline built using AWS S3, Snowflake (Snowpipe, Streams, Tasks, UDFs), and Power BI. This project automates ingestion of Yelp JSON datasets into Snowflake using Snowpipe, performs real-time transformations with Streams & Tasks, enriches reviews with sentiment analysis, and visualizes insights in Power BI Report Builder.

YELP Reviews data stream end-to-end data engineering pipeline using TCP/IP Socket, Apache Spark, OpenAI LLM, Kafka and Elasticsearch.

Production-grade MLOps data pipeline for Yelp restaurant review sentiment analysis with quality checks, preprocessing, augmentation, and performance benchmarking

About A real-time data pipeline project using Kafka, MongoDB, Elasticsearch, and PySpark. Streams raw data from Kafka, enriches it with sentiment analysis using Hugging Face models, stores results in MongoDB, and visualizes data in Elasticsearch with Kibana.

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Cloud ELT pipeline for Yelp review sentiment and business analysis using Python, AWS S3, and Snowflake.

A self-healing sentiment analysis pipeline for Yelp reviews powered by Ollama LLMs and Apache Airflow.

End-to-end pipeline analysing Yelp reviews using AWS S3, Snowflake, Python UDFs and advanced SQL sentiment analysis

Sentiment analysis of Yelp reviews using traditional NLP techniques and scikit-learn pipelines with Logistic Regression, Linear SVM, and Naive Bayes classifiers.

Big Data pipeline for sentiment analysis on Yelp restaurant reviews using Hadoop HDFS, Apache Spark MLlib, and a React dashboard powered by Supabase.

End-to-end data pipeline analyzing Yelp reviews using Python, AWS S3, and Snowflake. Features data preprocessing, cloud warehousing, and SQL-based sentiment analysis.

A production-ready, AI-powered data pipeline that automatically detects and heals data quality issues during sentiment analysis of Yelp reviews using Apache Airflow and Ollama LLM.

This project showcases a scalable data engineering pipeline for real-time sentiment analysis of Yelp reviews, using Docker, Spark, Kafka, and Elasticsearch. It integrates a large language model (LLM) for accurate analysis, enabling fast, flexible querying and insights into customer sentiment.

This repository contains a simple Python pipeline for sampling Yelp review data, merging it with precomputed sentiment‐analysis scores, and visualizing the relationship between user star ratings and sentiment polarity in a jittered scatter plot.

This project implements a scalable big data sentiment analysis pipeline using Apache Spark and Scala. Leveraging Spark’s distributed computing capabilities, it processes massive volumes of unstructured text data (Yelp reviews dataset, >5GB) to perform efficient sentiment classification (Positive, Negative, Neutral).

This project demonstrates a complete end-to-end sentiment analysis pipeline using Natural Language Processing (NLP) and a neural network built with TensorFlow/Keras. It processes and classifies customer reviews from Amazon, Yelp, and IMDB datasets to predict positive or negative sentiment.

Developed a Yelp review sentiment analysis pipeline using BERT, T5, and Word2Vec embeddings. Trained SVM, Logistic Regression, and CNN models, optimizing for key performance metrics (Accuracy, Precision, Recall, F1-Score). Built an inference engine to recommend restaurants based on sentiment predictions, enhancing decision support.

This project showcases an end-to-end pipeline analyzing Yelp reviews using Python, Amazon S3, and Snowflake. It applies AI-driven NLP for sentiment analysis to deliver key business insights from large-scale JSON data.

It id an End-to-end sentiment analysis pipeline using the Yelp Open Dataset, AWS S3, Snowflake, and Python UDFs. This project shows how to turn unstructured text reviews into structured, actionable insights - entirely inside the data warehouse.

Utilizing AWS S3, Glue, Athena, SageMaker, Kafka, and Spark, a scalable data pipeline was built to process Yelp reviews. Real-time insights were obtained by implementing sentiment analysis, privacy strategies (K-Anonymity, Differential Privacy), and streaming algorithms (Bloom Filter, LSH).

Built a complete data pipeline and analytics workflow to process, store, analyze, and visualize Yelp business reviews. Implemented automated data ingestion, cloud storage, Snowflake warehousing, SQL-based transformations, and sentiment analysis to uncover insights on customer satisfaction, business performance, and review patterns.

Built a pipeline to analyze 9,400+ Yelp reviews, performing sentiment classification and extracting actionable business insights using a GenAI reasoning engine with prompt engineering.Integrated OpenRouter API for multi-turn conversational analysis in Arabic and automated the workflow from raw feedback to structured recommendations.

This is a robust Cloud Data Pipeline for Yelp review analysis. It uses Python to split the large JSON into 10 files, stages data on AWS S3, and loads it into Snowflake. A Python UDF in Snowflake adds Sentiment Analysis (Positive/Negative/Neutral) to reviews, enabling deep, SQL-based analytics on the enriched data.

This is an end-to-end data analytics project built around the Yelp Open Dataset, performing both sentiment analysis and general data analysis. The goal is to demonstrate how to build a scalable data pipeline using real-world JSON data, cloud storage, Python-based file preprocessing, Snowflake as a data warehouse, and advanced SQL for analytics.