This project leverages on text data sourced from Twitter in addition to Natural Language Processing packages in R (E.g. qdap and tidytext) to perform basic Sentiment Analysis.
The objective will be to see if applying such methods to Twitter-sourced text data leads to any interesting insights. Future implementations might involve the integration of such findings with an algorithmic trading strategy to take positions based on market sentiment.
Due to conflicts between the twitteR and qdap packages, the code sections to query data and to analyze it are segregated into separate scripts.
First, git clone to a local folder with
git clone https://github.com/NicholasTanWeiHong/crude-oil-sentiment-analysis.git
Next, run twitter_query.R with custom API Keys - accessible by signing up for an account on Twitter Developers.
Finally, run the code in crude-oil-sentiment-analysis.R to perform Sentiment Analysis on the queried text data.
Alternatively, open crude-oil-sentiment-analysis.md for a report-style RMarkdown document.