Traditional sentiment analysis performs well in terms of polarity detection, but it cannot capture cognitive nuances needed for mental health monitoring. In this paper, we address two gaps in the current literature: the lack of task-driven guidance on how to choose transfer learning strategies and the lack of validation on cognitively rich da-tasets, other than standard polarity benchmarks. We present a systematic review of 150 transfer learning studies follow-ing PRISMA guidelines, summarising the findings into a decision framework that maps data availability, domain spec-ificity, and resource constraints to optimal model selection. We empirically validate our decision framework on a novel 4-class cognitive thought classification dataset with human-elicited samples and GPT-4 augmented data. Active learn-ing reduced annotation effort by 60% and achieved high inter-rater agreement (Fleiss’ Kappa = 0.82). According to our framework, 12 models were evaluated. BiLSTM-TF-IDF achieved 93.9% macro F1, outperforming transformer models by 0.5% with 9× less compute, supporting the medium-data/domain-specific path. Specifically, on our 4,944-sample domain-specific cognitive thought dataset using a single NVIDIA RTX 3090 GPU, BiLSTM-TF-IDF outperformed RoBERTa-base by 0.5% macro F1 (93.9% vs. 93.4%, McNemar p=0.004) via uncertainty-sampling active learning with SVM confidence threshold <0.40, while requiring approximately 9.4× less inference time and a 32× smaller model footprint (with a 6× lower peak GPU memory requirement). Uncertainty was quantified using least-confidence sam-pling (selecting samples where max class probability <0.40); the cross-entropy loss function was used for both the SVM-guided active learning and BiLSTM training. We also evaluate cross-dataset performance on IMDb and SST-2, demonstrating reasonable generalisability. Demographic analysis shows cognitive patterns that have implications for mental health. The dataset and code have been published at Zenodo (DOI: 10.5281/zenodo.17444289) to facilitate reproducible cognitive NLP research.