  Text data are being used as a lens through which human cognition can be
studied at a large scale. Methods like emotion analysis are now in the standard
toolkit of computational social scientists but typically rely on third-person
annotation with unknown validity. As an alternative, this paper introduces
online emotion induction techniques from experimental behavioural research as a
method for text-based emotion analysis. Text data were collected from
participants who were randomly allocated to a happy, neutral or sad condition.
The findings support the mood induction procedure. We then examined how well
lexicon approaches can retrieve the induced emotion. All approaches resulted in
statistical differences between the true emotion conditions. Overall, only up
to one-third of the variance in emotion was captured by text-based
measurements. Pretrained classifiers performed poorly on detecting true
emotions. The paper concludes with limitations and suggestions for future
research.
