I’m preparing a nationwide election campaign and need a reliable piece of software that can monitor and interpret voter sentiment in real-time. The immediate focus is social media—specifically Twitter—because that is where most of our target electorate voices opinions hour by hour. Here is what I’m after: • An end-to-end pipeline that automatically pulls tweets containing campaign-relevant keywords and hashtags, cleans the data, and classifies sentiment (positive, negative, neutral). • A dashboard that updates continuously, lets me filter by geography, time frame, and keyword, and presents easy-to-read trend graphs and word clouds. • The ability to export raw and aggregated data in CSV/JSON for deeper offline analysis. Python with Tweepy, pandas, scikit-learn, spaCy or similar NLP libraries is perfect, but I’m open to your preferred stack if it delivers equivalent accuracy and speed. Accuracy matters; I’ll spot-check a sample of the classified tweets to verify at least 80 % precision before sign-off. If you can build a modular architecture that allows Facebook or Instagram monitoring to be plugged in later, even better, but the MVP must nail Twitter first. Please outline your proposed approach, tech stack, and a realistic timeline when you reply, and feel free to showcase any comparable work.