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Scrapped tweets using twitter API (for keyword ‘Netflix’) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard

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rochitasundar/TwitterSentimentAnalysis-BigDataProject

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README.md

README.md

 
 

athena_queries.sql

athena_queries.sql

 
 

databricks_spark_sentiment_analysis.ipynb

databricks_spark_sentiment_analysis.ipynb

 
 

quicksight_final_dashboard.png

quicksight_final_dashboard.png

 
 

twitter_big_data_project.pdf

twitter_big_data_project.pdf

 
 

twitter_scrapper_netflix.py

twitter_scrapper_netflix.py

 
 

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Databricks - Spark Sentiment Analysis Notebook can be viewed here.

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Scrapped tweets using twitter API (for keyword ‘Netflix’) on an AWS EC2 instance, ingested data into S3 via kinesis firehose. Used Spark ML on databricks to build a pipeline for sentiment classification model and Athena & QuickSight to build a dashboard

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twitter-api aws-s3 python3 pyspark aws-ec2 twitter-sentiment-analysis aws-athena aws-kinesis-firehose count-vectorizer databricks-notebooks tf-idf-vectorizer accuracy-metrics aws-quicksight roc-auc-score multiclass-evaluator

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