A web platform for Contextualized Construct Representations (CCR) - theory-driven psychological text analysis (Atari, Omrani, et al.; EMNLP 2024). Built for the Culture and Morality Lab (UMass Amherst); this instance is the lab's dev/testing environment.

Upload a corpus (CSV/XLSX), pick a validated construct from the library or define your own (typed or uploaded from a file), choose a language and embedding model, run the analysis, inspect results (distributions, per-item loadings, top/bottom texts, data-quality warnings), and export everything - including a Python script that reproduces the run on any machine.

• Anonymous try-it tier: 3 runs/day, uploads deleted right after analysis, sessions purged after 24 h. Free accounts (email/password, optional Google sign-in) lift limits and keep your work (15 saved runs).
• Construct library (versioned, append-only, item-hashed) + custom constructs with reverse-scored flags; searchable grouped picker.
• Model registry: MiniLM default (the CCR reference model), E5-large-v2, Multilingual-E5; E5 prefix policy handled automatically; language coverage warnings.
• Structured data-quality warnings (language mismatch/uncertainty, short texts, truncation, duplicates, encoding fallback) - stable machine-readable codes.
• Per-run reproducibility: metadata JSON + offline-runnable script + pinned requirements. Corpus-embedding cache makes re-runs on the same corpus near-instant.
• Storage: local disk by default; S3-compatible (Cloudflare R2) via env config.

cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000 --env-file ../.env

Open http://127.0.0.1:8000. See MANUAL_TESTING.md for a full click-through test script and sample_data/README.md for what each sample file demonstrates. Configuration: copy .env.example to .env and fill what you need.

• Do not upload sensitive or identifiable data to this shared dev instance; anonymous storage is ephemeral and the instance may reset.
• The construct library ships the lab's full collection of 94 constructs. Item wording, reverse-scoring keys, subscale grouping, and citations were reviewed against the source publications in August 2026: 88 constructs are marked verified and carry the reviewer and review date. The other 6 stay flagged in the interface pending two wording decisions (the IPIP scales and the K10). See docs/specs/0007-construct-library-verification-pass.md.
• Tests: cd backend && CCR_FAKE_EMBEDDINGS=1 python -m pytest -q (174 tests, no ML downloads needed).

If you use the platform in research, cite it and the CCR method papers. The app's landing page has a "Cite this platform" link (APA, MLA, Chicago, Harvard, Vancouver, BibTeX, RIS), GitHub reads CITATION.cff for the same details, and the strings all come from backend/app/citation.py.

Anand, D., & Atari, M. (2026). CCR Platform: Theory-driven psychological text analysis with contextualized construct representation (Version 0.2.0) [Computer software]. Culture and Morality Lab, University of Massachusetts Amherst. https://psychologicaltextanalysis.com

Atari, M., Omrani, A., & Dehghani, M. (2023). Contextualized construct representation: Leveraging psychometric scales to advance theory-driven text analysis. PsyArXiv. https://doi.org/10.31234/osf.io/m93pd

Chen, Y., Li, S., Li, Y., & Atari, M. (2024). Surveying the dead minds: Historical-psychological text analysis with contextualized construct representation (CCR) for classical Chinese. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 2597-2615). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.151

Analyses run on a bundled example corpus also need that corpus's own citation, which the run metadata carries.

MIT (see LICENSE) covers the CODE. Data shipped alongside it does not inherit that licence:

• the questionnaire items in the construct library belong to their original authors, and each construct records its citation, source, and any redistribution caveat in rights_note;
• sample_data/camel_sample.csv is a slice of the CAMEL corpus (Zewail et al., 2026) and is CC BY-NC 4.0, not MIT. Non-commercial use with attribution; the citation and licence travel in the run metadata of any analysis run on it.