An exploration of NLP Sentiment Analysis of Twitter text and a sentiment metric published by the Chicago Federal Reserve Bank. There are some correlations but more historical Twitter data must be gathered and analyzed before the validity of any forecasting can be assessed.
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Comparison of Sentiment Analyses of Twitter Data and the Chicago Fed's CFBSC Outlook Metric
1. Matt McDermott
About
Data Scientist, Writer, Drummer
Project Description
● Data mine Twitter for Chicago publications
● NLP clean up & sentiment analysis on Twitter content
● Comparison with Chicago Fed’s CFBSC outlook metric
● ARIMA time-series analysis of sentiment analysis
Email address matthewmcdermott60515@gmail.com Cell number 872-235-9340
3. Question
● Are there correlations between an economic
indicator of business confidence and tweets from
newspapers? Can these suggest they occupy the
same mental ecosystem?
● Can a predictive model be meaningfully applied to
both?
4. Scope
Time Period
● January 2019 - March 2020
● 15 months/5 quarters
Federal Reserve Bank of Chicago
● CFSBC Survey by Chicago Fed
Twitter Data
● Twitter data from Crain’s Chicago Business,
Chicago Tribune, Chicago Sun-Times
5. Data Process
● Twint library (Python)
● Basic NLP clean up
● MatPlotLib Data Viz library
● ADF test
13. Next Steps
● Gather more data
○ Go back to 2015 or 2010
○ Include more publications
● Optimize my predictive model
● Further NLP analysis of collected tweets
15. Data
Twitter
● Crain’s - search term ‘Chicago’
○ 3,500 tweets
● Crain’s - search term ‘Business’
○ 2,145 tweets
● Tribune - search term ‘Chicago’
○ 14,135 tweets
● Tribune- search term ‘Business’
○ 9,486 tweets
● Sun-Times - search term ‘Chicago’
○ 8,129 tweets
● Sun-Times- search term ‘Business’
○ 8,086 tweets
Federal Reserve Bank of Chicago
● Chicago Fed Survey of Business Conditions
(CFSBC)
○ https://www.chicagofed.org/research/data/index