scores = k: sum(text.lower().count(w) for w in words) for k, words in concepts.items()
| Deep Feature | Description | |--------------|-------------| | | Ratio of imperative sentences + “how to” phrases | | Personal narrative density | Frequency of “I,” “my,” “I’ve learned” — Babauta often uses personal stories | | Negative-to-positive framing | e.g., “stop multitasking” (negative) vs “enjoy single-tasking” (positive) | | Minimalist instruction entropy | How many redundant vs. unique tips; Babauta often repeats core principles | 3. Extracted Deep Feature JSON Example "document_id": "leo_babauta_power_of_less.pdf", "structural_features": "page_count": 124, "sections_with_checklists": 8, "estimated_reading_time_min": 45 , "stylometric_features": "avg_sentence_length": 12.3, "flesch_kincaid_grade": 6.2, "first_person_pronoun_ratio": 0.07, "second_person_pronoun_ratio": 0.12 , "topic_signatures": [ "habit_stacking", "single_tasking", "letting_go_of_control", "daily_rituals" ], "deep_conceptual_features": "zen_influence_score": 0.91, "practical_productivity_focus": 0.94, "minimalist_philosophy_consistency": 0.89, "anti_hustle_culture": 0.85 , "behavioral_insights": "most_frequent_habit_advice": "start tiny", "most_frequent_obstacle_mentioned": "overwhelm", "solution_pattern": "reduce → focus → repeat"
You can use Python with libraries like:
