machine-learning

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Automation of Sentiment Analysis & Topic Modeling on Py-Spark & SparkNLP using Twitter big-data

Research Questions:

While working on this project, I specifically oriented my work, around the following questions keeping two electoral candidates in mind (Mr. Donald J. Trump & Mr. Joe Biden):

Is there a marginal difference in the ‘Twitter-holdings’ (user base) of both the candidates? (answered by: general Twitter analysis in sub-topic 1.2)
What ‘Sentiments’ are the candidates trying to drive from their daily tweets? (answered by: sentiment analysis of tweets using SparkNLP in sub-topic 2.2)
How are ‘User Behaving’ (by studying the user level tweet sentiments) on a day to day level towards these candidates? (answered by: sentiment analysis of tweets using SparkNLP in sub-topic 2.2)
What are the ‘Most Discussed Topics’ (top 3) by these candidates over Twitter? (answered by: LDA topic-modeling using SparkNLP in sub-topic 2.3)
Is there a way through which I could ‘Automate’ my analysis over a daily frequency? (answered by: local deployment of Models using windows cmd & connecting data to live open-source RDBMS PostgreSQL in sub-topic 3)