Leo Babauta | Pdf

| Feature Category | Example Deep Features for Leo Babauta PDF | |----------------|---------------------------------------------| | | Average sentence length (short: ~10-15 words), frequent use of “you,” “simple,” “less,” “focus,” “habit” | | Structural Markers | Number of bullet lists / checklists, presence of “Zen” headings, numbered steps (e.g., “1. Do one task at a time”) | | Readability | Flesch-Kincaid Grade Level (typically 6th–8th grade for Babauta), high personal pronoun density | | Lexical Themes | TF-IDF top terms: habit, distraction, mindfulness, clutter, daily routine, procrastination, gratitude | 2. Semantic & Conceptual Features (Deep) Use a small language model or keyword mapping to extract conceptual depth.

import PyPDF2 import textstat from sklearn.feature_extraction.text import TfidfVectorizer with open("leo_babauta.pdf", "rb") as f: reader = PyPDF2.PdfReader(f) text = " ".join([page.extract_text() for page in reader.pages]) Deep features readability = textstat.flesch_kincaid_grade(text) sentence_len = textstat.avg_sentence_length(text) Concept matching (simplified) concepts = "habit": ["habit", "routine", "daily", "practice"], "minimalism": ["less", "simplify", "declutter", "minimalist"], "mindfulness": ["mindful", "presence", "aware", "attention"] leo babauta pdf

# Example conceptual feature vector (pseudo-code) concept_scores = "minimalism": 0.87, "habit_formation": 0.92, "mindfulness_presence": 0.78, "anti_perfectionism": 0.65, "practical_zen": 0.88, "fear_procrastination": 0.71, "joy_in_simplicity": 0.83 | Feature Category | Example Deep Features for

| 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" import PyPDF2 import textstat from sklearn

You can use Python with libraries like:

scores = k: sum(text.lower().count(w) for w in words) for k, words in concepts.items()