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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Real time streaming and sentiment analysis of twitter data using kafka and pyspark

A Python/PySpark project for end-to-end streaming data processing. Ingests mock Twitter data via Kafka, performs real-time sentiment analysis using a Scikit-learn model in PySpark Structured Streaming, and saves results to MongoDB. Features a Flask API and Streamlit dashboard for live visualization.

This project offers a solution for real-time analysis of data streams from Twitter, enabling the prediction of sentiments expressed in tweets. The results are displayed on an intuitive graphical interface. The technologies used include Apache Kafka Streams, Apache Spark, PySpark, NLTK, MongoDB, Streamlit, and Docker.

It is a Lexicon based Twitter Sentiment Analysis project with two different approaches: a) Using Tweepy and TextBlob Libraries in Python to analyze any trending topic on Twitter and classify it based on Polarity score. b) Using Kafka producer, PySpark and Python to analyze live streaming data and classify the sentiments as positive and negetive. It also keeps track of the trend over a period of time, about how people's reaction change to a particular topic.