PT  - JOURNAL ARTICLE
AU  - Benjamin McMillan
AU  - Joshua Myers
AU  - An Nguyen
AU  - Don Robinson
AU  - Mark Kennard
TI  - Practical Applications of Analysis and Comparison of Natural Language Processing Algorithms as Applied to Bitcoin Conversations on Social Media
AID  - 10.3905/pa.2022.pa495
DP  - 2022 May 18
TA  - Practical Applications
PG  - pa.2022.pa495
4099  - https://pm-research.com/content/early/2022/05/11/pa.2022.pa495.short
4100  - https://pm-research.com/content/early/2022/05/11/pa.2022.pa495.full
AB  - In Analysis and Comparison of Natural Language Processing Algorithms as Applied to Bitcoin Conversations on Social Media, from the February 2022 issue of The Journal of Investing, authors Benjamin McMillan, Joshua Myers (both of IDX Digital Assets), An Nguyen (of Johns Hopkins University), Don Robinson, and Mark Kennard (both of Palladiem) studied how natural language processing (NLP) algorithms capture investor sentiment online and whether changes in sentiment can predict investment returns. The authors used four different NLP algorithms to measure trends in sentiment toward bitcoin on two Reddit message boards, r/bitcoin (which focuses on bitcoin) and r/investing (which covers general investments). The authors used sentiment analysis to determine whether positive or negative posts about bitcoin correlated with bitcoin’s short-term performance.The authors found a statistically significant correlation between negative posts on r/bitcoin and bitcoin price changes. Bitcoin’s performance tended to drop in the five days before a negative post and rise in the five days afterward. There was no such correlation for positive posts on r/bitcoin or for any posts on r/investing. Therefore, clusters of negative posts on r/bitcoin may mark a local bottom and be predictive of increased performance.