AIS Transactions on Replication Research
Abstract
This paper replicates text-based Big Five personality score predictions generated by the Receptiviti API—a tool built on and tied to the popular psycholinguistic analysis tool Linguistic Inquiry and Word Count (LIWC). We use four social media datasets with posts and personality scores for nearly 9,000 users to determine the accuracy of the Receptiviti predictions. We found Mean Absolute Error rates in the 15–30% range, which is a higher error rate than other personality prediction algorithms in the literature. Preliminary analysis suggests relative scores between groups of subjects may be maintained, which may be sufficient for many applications.
Recommended Citation
Golbeck, Jennifer Ann
(2016)
"Predicting Personality from Social Media Text,"
AIS Transactions on Replication Research: Vol. 2, Article 2.
DOI: 10.17705/1atrr.00009
Available at:
https://aisel.aisnet.org/trr/vol2/iss1/2
DOI
10.17705/1atrr.00009