  This research introduces a Positive Reconstruction Framework based on
positive psychology theory. Overcoming negative thoughts can be challenging,
our objective is to address and reframe them through a positive
reinterpretation. To tackle this challenge, a two-fold approach is necessary:
identifying cognitive distortions and suggesting a positively reframed
alternative while preserving the original thought's meaning. Recent studies
have investigated the application of Natural Language Processing (NLP) models
in English for each stage of this process. In this study, we emphasize the
theoretical foundation for the Positive Reconstruction Framework, grounded in
broaden-and-build theory. We provide a shared corpus containing 4001 instances
for detecting cognitive distortions and 1900 instances for positive
reconstruction in Mandarin. Leveraging recent NLP techniques, including
transfer learning, fine-tuning pretrained networks, and prompt engineering, we
demonstrate the effectiveness of automated tools for both tasks. In summary,
our study contributes to multilingual positive reconstruction, highlighting the
effectiveness of NLP in cognitive distortion detection and positive
reconstruction.
