I have a corpus of fewer than 10,000 text excerpts that must be annotated for sentiment analysis, and I want to use this project to refine the entire workflow at the same time. The goal is two-fold: deliver a clean, consistently labeled dataset (positive, neutral, negative, or a scheme you suggest) and document an efficient, scalable method that my team can reuse when the volume inevitably grows. You will begin by reviewing the current approach and identifying friction points—duplicate effort, unclear guidelines, or tooling gaps. From there, I’d like you to propose a lightweight pipeline that could include anything from rule-based pre-tagging scripts to a streamlined interface in tools such as Prodigy, Label Studio, or a custom spreadsheet template. Whichever route you recommend, please ensure it supports easy export to CSV or JSON with standard columns for text, sentiment label, annotator ID, and time stamp. Deliverables • A fully labeled dataset of all text entries, ready for immediate ingestion by a sentiment analysis model • A concise “playbook” (Markdown or PDF) outlining setup, annotation guidelines, reviewer checks, and suggestions for scaling beyond 10k texts • Optional scripts or config files used to accelerate or validate the work I’ll provide the raw text in whichever format you prefer (CSV, TXT, or via API). Let me know which tooling stack you’re most comfortable with and how you plan to ensure inter-annotator reliability—simple agreement metrics are enough at this stage. If you have experience merging human annotation with light automation, that will be a plus. Feel free to share a brief note on similar projects you’ve completed and how long you expect each phase to take.