[marvinkennis]

I just did a project on this, but for Bitcoin instead of stocks where I examined news, Reddit, forum (Bitcointalk.org) and IRC sentiment using some simple ML algos. The goal was to determine whether this data has any predictive causality.

I scraped the above sources over a full year (2015) and then had the data annotated on positive, negative and neutral sentiment.

The problem with labeling sentiment data is that there might not be a single 'true' label due to varying interpretations and ambiguity. So at best you'll get to 80-85% accuracy there. The less formal (News > Reddit/Forum > IRC), the lower your accuracy due to lack of context.

I Then matched the annotated sentiment to market data and did some causality analysis. What I found is that interestingly, you can't just say positive news = price/volume goes up. It is way more fine grained than that. For example negative Reddit sentiment leads price movements, but price movements lead positive sentiment. For news its the reverse.

All in all I didn't incorporate this into any trading strategies, but found it interesting to see the differences between online sentiment channels.