Twitter Sentiment Analysis on trending stocks using Big Data Architecture

I am looking to complete a Twitter sentiment analysis project on trending stocks in the market. This project requires expertise in Big Data Architecture. This project requires real-time tweets to be fetched based on top trending tweets, and sentiments analysis is done to get some insights into the stock market. I need something similar to https://github.com/shafiab/HashtagCashtag?trk=public_post_comment-text
Specifications:
Fetch top 10 trending stocks on NASDAQ (Using Nasdaq API) and NYSE in that instance of time.
Price information for each stock will be fetched from www.netfonds.no
Fetch tweets mentioning these 20 stocks (top 10 from NYSE and top 10 NASDAQ) from twitter (using twitter API) .
Keep fetching x number of tweets for each stock in very short intervals of time to make the data stream live(continuously).
Use kafka for data ingestion.
Store the data coming from kafka into Amazon S3/gcloud storage
Data From Amazon S3 will go to spark for processing
Refer https://github.com/shafiab/HashtagCashtag?trk=public_post_comment-text for processing in spark
Use Cassandra/MongoDB as output sink for spark
Frontend visualizing results
Containerize using docker
Host the project on AWS/GCP