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Sentiment-Analysis

This project does sentiment analysis using twitter data. It's a web application that employs spark engine to run a python program in the backend to compute sentiment analysis using real-time twitter data. The spark jobs are submitted by the user usign the website after providing a topic for the sentiment analysis.

The project uses: -Spark engine to perform sentiment analysis on the real time twitter data. -Third party api, TextBlob, which uses Naive Bayes classifier internally to compute the sentiment. -ElastiSearch database and Kibana Dashboad which was hosted on the cloud (I hosted it using elasticsearch website for 15 days trial period).

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An application to analyze sentiments of a particular topic using Spark and real-time Twitter feeds (i.e. positive, negative, and neutral). Elasticsearch and Kibana were utilized for storage and visualization respectively

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