News article headlines about stocks were scraped from Tiingo and a sentiment analysis was performed using both nltk and TextBlob.
The graphs are made using plotly, which doesn't load on GitHub, so pasting the link to the Pasting the link of the Jupyter Notebook into https://nbviewer.jupyterorg/ will allow one to view them.
Twitter's API was used to gather tweets about the 'Great Reset' as it was trending on social media when this project was being worked on and I thought that it would be controversial enough to get a wide range of opinions on the subject. A sentiment analysis was then done using TextBlob.
- great-reset-2000.csv = 2000 tweets containing the phrase 'great reset'
- vote-fraud-1000.csv = 1000 tweets containing the phrase 'voter fraud'
Reddit's API was used to gather post titles and comments from r/worldnews. NLTK was used to perform a sentiment analysis of the title and comments of the post. Finally, graphs were created analyzing the cleaned data. The number of words, length of words, and frequency of profanity in the headlines and comments were also analyzed.
The csv to look at contains the posts title, followed by its' polarity score, then each comment on the post, followed by its' polarity score. It may be too large to view on GitHub, so one could download vd (pip install visidata) to preview the csv quickly from the command line.