Market Trend Prediction using Sentiment Analysis:Lessons Learned and Paths Forward
Table 10. Market trend prediction using financial tweets from Twitter (01/04/2014 – 01/04/2015).
['Type', 'baseline Acc', 'baseline  [ITALIC] Fup1', 'baseline  [ITALIC] Fdown1', 'all+attitude+emotion Acc', 'all+attitude+emotion  [ITALIC] Fup1', 'all+attitude+emotion  [ITALIC] Fdown1', 'all+emotion Acc', 'all+emotion  [ITALIC] Fup1', 'all+emotion  [ITALIC] Fdown1', 'filtering+emotion Acc', 'filtering+emotion  [ITALIC] Fup1', 'filtering+emotion  [ITALIC] Fdown1']
Only for the JPM stock we could see noticeable performance improvements in the “filtering+emotion” setting. Our results have also confirmed that sentiment attitudes on their own are probably not very useful for market trend prediction, but at least for some particular stocks sentiment emotions could be exploited to improve machine learning models like LSTM to get better market trend prediction.