My Insight Data Engineering Fellowship project. I implemented a big data processing pipeline based on ​lambda architecture​, that aggregates Twitter and US stock market data for user sentiment analysis using open source tools - ​Apache Kafka ​for data ingestions, Apache Spark ​& ​Spark Streaming ​for batch & real-time processing, ​Apache Cassandra f​ or storage, ​Flask​, ​Bootstrap and ​HighCharts f​ or frontend.

Twitter Sentiment Analysis using Spark and Kafka

A real-time streaming ETL pipeline for streaming and performing sentiment analysis on Twitter data using Apache Kafka, Apache Spark and Delta Lake.

Personal project where I perform some analytics (including Sentiment Analysis) over a Twitter Stream using Big Data Technologies of the Hadoop echosystem such as Flume, Kafka, and Spark Streaming.

Ingesting real-time Twitter API using tweepy into Kafka and process using Apache Spark Structured Streaming with Sentiment Analysis TextBlob before loading into time-series database, InfluxDB and monitoring dashboard, Grafana

Implemented the following framework using Apache Spark Streaming, Kafka, Elastic, and Kibana. The framework performs SENTIMENT analysis of hash tags in twitter data in real-time. For example, we want to do the sentiment analysis for all the tweets for #trump, #coronavirus.

Big data sentiment analysis pipeline using Apache Kafka, Spark Streaming, and HBase. Demonstrates ingestion and real-time processing of Twitter streams, basic sentiment scoring, and persistent storage for querying and visualization.

Spark streaming application that will perform sentiment analysis using NLP and Kafka. Sentiments are visualized using Kibana, Elasticsearch and Logstash.

Twitter Sentiment Analysis using Kafka Connect, Spark Streaming, Apache Avro, MLlib and Stanford CoreNLP

Sentiment analysis of crypto tweets using Twitter Streaming API, Kafka, Spark, and Cassandra built on top of docker compose

personal project to pull live Twitter data using Nifi getTwitter processor and pushes to Kafka topic which is then consumed by a Spark Streaming application where basic sentiment analysis is performed and the final result is stored in elastic search for visualization using Kibana.

Sentiment Analysis after consuming Twitter Stream Data - Technology Used - Python, Apache Kafka and Spark Streaming

Real-Time Sentiment Analysis on Twitter Streams is a web application that categorizes tweets into sentiments like Negative, Positive, Neutral, or Irrelevant. Built using Apache Kafka , Spark and PySpark ML models, it offers real-time analysis capabilities.

Real-Time Sentiment Analysis on Twitter Streams is a web application that categorizes tweets into sentiments like Negative, Positive, Neutral, or Irrelevant. Built using Apache Kafka , Spark and PySpark ML models, it offers real-time analysis capabilities.

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Twitter Sentiment Analysis using Spark Streaming and Kafka

Twitter sentiment analysis using Kafka and Spark Streaming

Twitter sentiment analysis using Apache Kafka, Apache Spark structured streaming and dashboard monitoring page.

Real-time Twitter sentiment analysis using Apache Kafka, Spark Structured Streaming, and BERT (HuggingFace Transformers).

A real-time data pipeline for Twitter sentiment analysis using Apache Kafka, Spark Structured Streaming, and MongoDB.

a real-time sentiment analysis application for the tweet data stream from Twitter API, built using Apache Spark, Kafka, and Node.js

A sentiment Analysis of real time tweets using Spark’s stream processing API, Apache Kafka and Twitter API to receive live tweets.

A real time streaming ETL pipeline for Twitter data implemented using Apache Kafka, Apache Spark and Delta Lake Database. Sentiment analysis is performed using Spark Machine Learning libraries on the streaming data, before being written to the database.

Real-time and batch Twitter sentiment analysis pipeline using Kafka, Spark, Vertex AI, Flask, and Docker. Simulates streaming tweet ingestion, classifies sentiment, and provides interactive dashboards for insights and trend monitoring. Easy to run with Docker.

Real-time disaster prediction system using Kafka and Spark Structured Streaming with ML-based flood detection (UNet), weather risk analysis, and Twitter sentiment classification. Event-driven, scalable architecture with live dashboard.

Performed sentiment analysis on tweets posted in real-time using twitter’s API. Stored the tweets using Apache Kafka in the intermediate step and used Apache Spark streaming to process them. Stored the output sentiment using Elastic Search indices and visualized the results using Kibana.

This project use Apache Spark (Structured Streaming/kafka) to process real-time Twitter data, perform sentiment analysis using NLTK/TextBlob, and store results in a data lake (Delta Lake) and a database (PostgreSQL/Elasticsearch) for visualization with Kibana.