Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis

TL;DR

This paper explores how multi-task learning models based on Cognitive Appraisal Theory can improve the prediction of user post-purchase intentions by incorporating psychological traits and language features, surpassing traditional text-only models.

Contribution

It introduces a novel multi-task learning framework grounded in Cognitive Appraisal Theory that integrates psychological attributes to enhance user behavior prediction in NLP.

Findings

Language and traits improve prediction accuracy

Psychological constructs enhance model performance

Implications for computational psychology applications

Abstract

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.