Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis
This work addresses the challenge of enhancing user behavior prediction in NLP for applications in computational psychology, though it appears incremental as it builds on existing multi-task learning approaches.
The study tackled the problem of predicting user behavior from text by integrating psychological attributes and language using multi-task learning based on Cognitive Appraisal Theory, resulting in improved prediction performance beyond text-only models.
Supervised machine-learning models for predicting user behavior offer a challenging classification problem with lower average prediction performance scores than other text classification tasks. This study evaluates multi-task learning frameworks grounded in Cognitive Appraisal Theory to predict user behavior as a function of users' self-expression and psychological attributes. Our experiments show that users' language and traits improve predictions above and beyond models predicting only from text. Our findings highlight the importance of integrating psychological constructs into NLP to enhance the understanding and prediction of user actions. We close with a discussion of the implications for future applications of large language models for computational psychology.