#!/usr/bin/env python3 """ analyze_titles.py - 分析YouTube频道视频标题模式 分析维度： - 标题长度分布 - 数字使用频率与类型 - 高频词汇（去除停用词） - 标题公式分类（挑战型/数字型/悬念型/对比型/情感型） - 标点符号与大写模式 用法: python analyze_titles.py titles.txt python analyze_titles.py titles.txt -o report.md python analyze_titles.py titles.txt --top 30 输入格式: 纯文本文件，每行一个标题 输出格式: Markdown分析报告 """ import argparse import re import sys from collections import Counter from pathlib import Path # 英文停用词（轻量级，不依赖nltk） STOP_WORDS = { "i", "me", "my", "we", "our", "you", "your", "he", "him", "his", "she", "her", "it", "its", "they", "them", "their", "a", "an", "the", "and", "but", "or", "for", "nor", "on", "at", "to", "from", "by", "in", "of", "with", "is", "am", "are", "was", "were", "be", "been", "being", "have", "has", "had", "do", "does", "did", "will", "would", "shall", "should", "may", "might", "must", "can", "could", "not", "no", "so", "if", "then", "than", "that", "this", "these", "those", "what", "which", "who", "whom", "how", "when", "where", "why", "all", "each", "every", "both", "few", "more", "most", "other", "some", "such", "only", "same", "too", "very", "just", "about", "above", "after", "again", "also", "any", "because", "before", "between", "during", "into", "through", "up", "down", "out", "over", "under", "here", "there", "now", "get", "got", "go", "going", "went", "make", "made", "like", "even", "still", "back", "us", } # 标题公式分类的关键词/模式 TITLE_PATTERNS = { "挑战型": [ r"\b(challenge|survive|last|endure|spent|living|tried|attempt)\b", r"\b(\d+)\s*(hours?|days?|minutes?)\b", r"\bi\s+(survived|built|made|ate|bought|opened|spent)\b", ], "数字型": [ r"^\$[\d,]+", r"\$[\d,]+\s+vs\.?\s+\$[\d,]+", r"\b\d{2,}\b", r"\b(100|1000|10000|million|billion)\b", ], "悬念型": [ r"\.\.\.", r"\?$", r"\b(mystery|secret|hidden|never|impossible|insane|unbelievable)\b", r"\b(what happens|you won't believe|no one|nobody)\b", ], "对比型": [ r"\bvs\.?\b", r"\bversus\b", r"\$[\d,]+\s+vs\.?\s+\$[\d,]+", r"\b(cheap|expensive|worst|best|biggest|smallest)\b.*\bvs\.?\b", r"\b(world'?s?\s+(largest|smallest|most|least|biggest|cheapest))\b", ], "情感型": [ r"\b(emotional|crying|tears|heartwarming|giving|donated|surprise)\b", r"!{2,}", r"\b(amazing|incredible|insane|crazy|epic|extreme)\b", ], } def load_titles(filepath: str) -> list[str]: """从文本文件加载标题，每行一个""" path = Path(filepath) if not path.exists(): print(f"[ERROR] 文件不存在: {filepath}", file=sys.stderr) sys.exit(1) titles = [ line.strip() for line in path.read_text(encoding="utf-8").splitlines() if line.strip() ] if not titles: print(f"[ERROR] 文件为空: {filepath}", file=sys.stderr) sys.exit(1) return titles def analyze_length(titles: list[str]) -> dict: """分析标题长度分布""" lengths = [len(t) for t in titles] word_counts = [len(t.split()) for t in titles] return { "char_avg": sum(lengths) / len(lengths), "char_min": min(lengths), "char_max": max(lengths), "char_median": sorted(lengths)[len(lengths) // 2], "word_avg": sum(word_counts) / len(word_counts), "word_min": min(word_counts), "word_max": max(word_counts), "brackets": [ sum(1 for l in lengths if l <= 30), sum(1 for l in lengths if 30 < l <= 50), sum(1 for l in lengths if 50 < l <= 70), sum(1 for l in lengths if l > 70), ], } def analyze_numbers(titles: list[str]) -> dict: """分析数字使用情况""" has_number = [t for t in titles if re.search(r"\d", t)] has_dollar = [t for t in titles if "$" in t] numbers_found = [] for t in titles: numbers_found.extend(int(n.replace(",", "")) for n in re.findall(r"[\d,]+", t) if n.replace(",", "").isdigit()) return { "with_number_pct": len(has_number) / len(titles) * 100, "with_dollar_pct": len(has_dollar) / len(titles) * 100, "common_numbers": Counter(numbers_found).most_common(10), } def analyze_words(titles: list[str], top_n: int = 20) -> list[tuple[str, int]]: """提取高频词汇（去除停用词）""" words = [] for t in titles: tokens = re.findall(r"[a-zA-Z]+", t.lower()) words.extend(w for w in tokens if w not in STOP_WORDS and len(w) > 1) return Counter(words).most_common(top_n) def classify_titles(titles: list[str]) -> dict[str, list[str]]: """按标题公式分类""" results = {cat: [] for cat in TITLE_PATTERNS} for t in titles: for cat, patterns in TITLE_PATTERNS.items(): if any(re.search(p, t, re.IGNORECASE) for p in patterns): results[cat].append(t) break # 每个标题只归入第一个匹配的类别 results["其他"] = [ t for t in titles if not any(t in v for v in results.values()) ] return results def analyze_punctuation(titles: list[str]) -> dict: """分析标点和大写模式""" return { "ends_exclamation": sum(1 for t in titles if t.endswith("!")), "ends_question": sum(1 for t in titles if t.endswith("?")), "ends_ellipsis": sum(1 for t in titles if t.endswith("...")), "has_all_caps_word": sum(1 for t in titles if re.search(r"\b[A-Z]{2,}\b", t)), "has_emoji": sum(1 for t in titles if re.search(r"[\U0001F600-\U0001F9FF]", t)), } def generate_report(titles: list[str], top_n: int) -> str: """生成Markdown分析报告""" total = len(titles) length_stats = analyze_length(titles) number_stats = analyze_numbers(titles) top_words = analyze_words(titles, top_n) categories = classify_titles(titles) punct_stats = analyze_punctuation(titles) lines = [] lines.append(f"# YouTube标题分析报告\n") lines.append(f"共分析 **{total}** 个标题\n") # 长度分布 lines.append("## 1. 标题长度分布\n") lines.append("| 指标 | 字符数 | 词数 |") lines.append("|------|--------|------|") lines.append(f"| 平均 | {length_stats['char_avg']:.1f} | {length_stats['word_avg']:.1f} |") lines.append(f"| 最短 | {length_stats['char_min']} | {length_stats['word_min']} |") lines.append(f"| 最长 | {length_stats['char_max']} | {length_stats['word_max']} |") lines.append(f"| 中位数 | {length_stats['char_median']} | - |") lines.append("") b = length_stats["brackets"] lines.append(f"- 30字符以内: {b[0]} ({b[0]/total*100:.1f}%)") lines.append(f"- 31-50字符: {b[1]} ({b[1]/total*100:.1f}%)") lines.append(f"- 51-70字符: {b[2]} ({b[2]/total*100:.1f}%)") lines.append(f"- 70字符以上: {b[3]} ({b[3]/total*100:.1f}%)") lines.append("") # 数字使用 lines.append("## 2. 数字使用\n") lines.append(f"- 含数字的标题: {number_stats['with_number_pct']:.1f}%") lines.append(f"- 含$金额的标题: {number_stats['with_dollar_pct']:.1f}%") if number_stats["common_numbers"]: lines.append("\n常见数字:") for num, count in number_stats["common_numbers"]: lines.append(f" - {num:,}: 出现 {count} 次") lines.append("") # 高频词汇 lines.append(f"## 3. 高频词汇 (Top {top_n})\n") lines.append("| 排名 | 词汇 | 出现次数 |") lines.append("|------|------|----------|") for i, (word, count) in enumerate(top_words, 1): lines.append(f"| {i} | {word} | {count} |") lines.append("") # 标题公式分类 lines.append("## 4. 标题公式分类\n") lines.append("| 类型 | 数量 | 占比 | 示例 |") lines.append("|------|------|------|------|") for cat in ["挑战型", "数字型", "悬念型", "对比型", "情感型", "其他"]: items = categories.get(cat, []) pct = len(items) / total * 100 if total else 0 example = items[0][:50] + "..." if items and len(items[0]) > 50 else (items[0] if items else "-") lines.append(f"| {cat} | {len(items)} | {pct:.1f}% | {example} |") lines.append("") # 标点与格式 lines.append("## 5. 标点与格式特征\n") lines.append(f"- 感叹号结尾: {punct_stats['ends_exclamation']} ({punct_stats['ends_exclamation']/total*100:.1f}%)") lines.append(f"- 问号结尾: {punct_stats['ends_question']} ({punct_stats['ends_question']/total*100:.1f}%)") lines.append(f"- 省略号结尾: {punct_stats['ends_ellipsis']} ({punct_stats['ends_ellipsis']/total*100:.1f}%)") lines.append(f"- 含全大写词: {punct_stats['has_all_caps_word']} ({punct_stats['has_all_caps_word']/total*100:.1f}%)") lines.append("") # 洞察 lines.append("## 6. 关键洞察\n") # 自动生成一些洞察 if number_stats["with_number_pct"] > 60: lines.append("- **数字驱动**: 超过60%的标题使用数字，数字是核心吸引力元素") if number_stats["with_dollar_pct"] > 30: lines.append("- **金钱叙事**: 大量使用$金额，制造价值感和规模感") dominant_cat = max( [(cat, len(items)) for cat, items in categories.items() if cat != "其他"], key=lambda x: x[1], ) lines.append(f"- **主导公式**: 「{dominant_cat[0]}」是使用最多的标题类型 ({dominant_cat[1]}/{total})") if length_stats["char_avg"] < 50: lines.append("- **简洁风格**: 平均标题长度不到50字符，倾向短标题") else: lines.append("- **详细风格**: 平均标题超过50字符，倾向描述性标题") return "\n".join(lines) def main(): parser = argparse.ArgumentParser( description="分析YouTube频道视频标题模式", formatter_class=argparse.RawDescriptionHelpFormatter, epilog="示例:\n python analyze_titles.py mrbeast_titles.txt\n python analyze_titles.py titles.txt -o report.md --top 30", ) parser.add_argument("input", help="标题文本文件（每行一个标题）") parser.add_argument("-o", "--output", help="输出报告文件路径（默认打印到终端）") parser.add_argument("--top", type=int, default=20, help="显示的高频词数量（默认20）") args = parser.parse_args() titles = load_titles(args.input) report = generate_report(titles, args.top) if args.output: Path(args.output).write_text(report, encoding="utf-8") print(f"[OK] 报告已保存到: {args.output}") else: print(report) if __name__ == "__main__": main()