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.

翻译：本研究基于积极心理学理论，提出了一个积极重构框架。克服负面思维可能具有挑战性，我们的目标是通过积极的重新诠释来应对并重构这些思维。为应对这一挑战，需要采取双重方法：识别认知扭曲，并在保留原意的前提下提出一个积极重构的替代表述。近期研究探讨了将自然语言处理模型应用于英语语境下该过程的各个阶段。在本研究中，我们着重阐述了基于拓展-建构理论的积极重构框架的理论基础。我们提供了一个共享语料库，包含4001个用于检测认知扭曲的实例和1900个用于中文积极重构的实例。利用包括迁移学习、预训练网络微调和提示工程在内的最新自然语言处理技术，我们证明了自动化工具在这两项任务中的有效性。总之，我们的研究为多语言积极重构做出了贡献，突显了自然语言处理在认知扭曲检测与积极重构中的有效性。