I have a growing collection of Telugu customer-review text that needs to be distilled into concise, one-line summaries and tagged as Positive, Negative, or Neutral. The result I need is a clean JSON output per record, so each review comes back with its summary and sentiment label in a machine-readable format. Because the language is highly nuanced, I’d like you to blend both rule-based and machine-learning techniques: think lexicon cues for idiomatic Telugu alongside a fine-tuned transformer or any other classifier that lifts accuracy. Feel free to draw on pretrained Telugu-BERT, FastText, spaCy, custom dictionaries—whatever combination you believe delivers the most reliable hybrid model. Deliverables • Python or notebook script that ingests raw Telugu text and produces the JSON format • Trained model files (and any custom lexicons) with version control • README explaining setup, dependencies, and how to retrain or update the model • Brief validation report: precision, recall, and overall accuracy on a held-out test set of the same domain Acceptance criteria The pipeline must run end-to-end on my sample dataset and reach a sentiment-classification F1 score of at least 0.80 while generating legible summaries that preserve key points from each review.