494 lines
12 KiB
Plaintext
494 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 用Python分析《美女与野兽》"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"在一篇最近发表的论文*A quantitative analysis of gendered compliments in Disney Princess films*中，Carmen Fought和Karen Eisenhauer发现在这部迪士尼经典影片中女性角色的对话要多于迪士尼近期的电影作品。作者在网络上发现了美女与《野兽》的脚本，因此我立刻用Python重做了他们的分析。\n",
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"<br />更多地，我在文章最后加入了对《玩具总动员》的分析，这个脚本的形式完全不同，但其中91%的对白来自男性角色。"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"点击下边的cell，点击上方工具栏里的执行图标，即可执行代码块，看到输出结果。代码块左边的In[]出现In[*]表示代码正在执行"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from __future__ import division\n",
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"\n",
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"import re\n",
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"from collections import defaultdict\n",
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"\n",
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"import requests\n",
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"import pandas as pd\n",
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"import matplotlib\n",
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"\n",
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"%matplotlib inline\n",
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"matplotlib.style.use('ggplot')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Load the script which comes as a text file\n",
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"\n",
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"script_url = 'http://www.fpx.de/fp/Disney/Scripts/BeautyAndTheBeast.txt'\n",
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"script = requests.get(script_url).text"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"我们看下脚本的开篇："
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Let's look at the beginning of the script\n",
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"\n",
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"script.splitlines()[:20]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"再在中间随意选取一段："
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Let's look at a random place\n",
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"\n",
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"script.splitlines()[500:520]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"看上去很容易分析，因为角色和对白间用:隔开"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# seems fairly easy to parse since \n",
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"# each new speaking line has : and begins with all caps\n",
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"\n",
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"def remove_spaces(line):\n",
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" # remove the weird spaces\n",
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" return re.sub(' +',' ',line)\n",
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"\n",
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"def remove_paren(line):\n",
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" # remove directions that are not spoken\n",
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" return re.sub(r'\\([^)]*\\)', '', line)\n",
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"\n",
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"\n",
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"lines = []\n",
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"line = ''\n",
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"for row in script.splitlines():\n",
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" if ': ' in row and row[:3].upper() == row[:3]:\n",
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" line = remove_spaces(line)\n",
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" line = remove_paren(line)\n",
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" lines.append(line)\n",
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" line = row\n",
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" elif ' ' in row:\n",
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" line = line + ' ' + row.lstrip()\n",
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"# don't forget the last line\n",
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"lines.append(remove_spaces(line))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"lines[:15]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"看看结尾什么样："
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# How does the end look\n",
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"\n",
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"lines[-5:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# 我们去掉可能的空白行\n",
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"\n",
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"print (len(lines))\n",
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"lines = [l for l in lines if len(l) > 0]\n",
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"print (len(lines))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"现在，我们找出所有角色，并计算他们的出场次数（对白数）"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# now figure out the roles and how many times they appear\n",
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"\n",
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"roles = defaultdict(int)\n",
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"\n",
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"for line in lines:\n",
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" # take advantage of the fact that the speaker is always listed before the :\n",
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" speaker = line.split(':')[0]\n",
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" roles[speaker] = roles[speaker] + 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"len(roles)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"看一下每个角色出现的相对频率："
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# take a look at the relative frequency of each role\n",
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"roles"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"看起来有一行“to think about”是乱入的（恰好满足了parse条件），我们忽略它"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Looks like there is one bum line ('to think about'')\n",
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"# But I'll ignore that for now.\n",
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"\n",
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"# Quickly eye ball which roles are female and which are possibly mixed groups.\n",
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"\n",
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"females = ['WOMAN 1',\n",
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" 'WOMAN 2',\n",
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" 'WOMAN 3',\n",
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" 'WOMAN 4',\n",
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" 'WOMAN 5',\n",
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" 'OLD CRONIES',\n",
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" 'MRS. POTTS',\n",
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" 'BELLE',\n",
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" 'BIMBETTE 1'\n",
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" 'BIMBETTE 2',\n",
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" 'BIMBETTE 3']\n",
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"\n",
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"groups = ['MOB',\n",
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" 'ALL',\n",
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" 'BOTH']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"将每一行对白根据角色性别进行标记，并统计不同性别的对白数量"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Mark each line of dialogue by sex and count them\n",
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"\n",
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"sex_lines = {'Male': 0,\n",
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" 'Female': 0}\n",
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"\n",
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"for line in lines:\n",
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" # Extract speaker \n",
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" speaker = line.split(':')[0]\n",
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" \n",
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" if speaker in females:\n",
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" sex_lines['Female'] += 1\n",
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" \n",
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" elif sex_lines not in groups:\n",
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" sex_lines['Male'] += 1\n",
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"\n",
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"print (sex_lines)\n",
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"print (sex_lines['Male']/(sex_lines['Male'] + sex_lines['Female']))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"我们使用一张图来显示结果："
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Quick graphical representation \n",
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"\n",
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"df = pd.DataFrame([sex_lines.values()],columns=sex_lines.keys())\n",
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"df.plot(kind='bar')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"也许男性角色和女性角色的对白长度有明显不同？我们来看一看<br/>这次我们计算对白中单词数量而不是计算对白次数："
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Maybe men and women talk for different lengths? This counts words instead of \n",
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"\n",
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"sex_words = {'Male': 0,\n",
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" 'Female': 0}\n",
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"\n",
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"for line in lines:\n",
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" speaker = line.split(':')[0]\n",
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" dialogue = line.split(':')[1] \n",
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" # remove the \n",
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" # tokenize sentence by spaces\n",
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" word_count = len(dialogue.split(' ')) \n",
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" \n",
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" if speaker in females:\n",
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" sex_words['Female'] += word_count\n",
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" elif speaker not in groups:\n",
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" sex_words['Male'] += word_count\n",
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"\n",
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"print (sex_words)\n",
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"print (sex_words['Male']/(sex_words['Male'] + sex_words['Female']))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"也用图表显示出来："
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Quick graphical representation \n",
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"\n",
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"df = pd.DataFrame([sex_words.values()],columns=sex_words.keys())\n",
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"df.plot(kind='bar')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"下面是额外的《玩具总动员》的分析"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# Bonus toy story analysis\n",
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"\n",
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"url = 'http://www.dailyscript.com/scripts/toy_story.html'\n",
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"toy_story_script = requests.get(url).text\n",
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"\n",
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"# toy_story_script.splitlines()[250:350]\n",
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"\n",
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"lines = []\n",
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"speaker = ''\n",
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"dialogue = ''\n",
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"for row in toy_story_script.splitlines()[90:]:\n",
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" if ' ' in row: \n",
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" if ':' not in speaker:\n",
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" lines.append( {'Speaker': remove_paren(speaker).strip(),\n",
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" 'Dialogue': remove_paren(dialogue).strip() } )\n",
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" \n",
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" speaker = remove_spaces(row.strip())\n",
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" dialogue = ''\n",
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" elif ' ' in row:\n",
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" dialogue = dialogue + ' ' + remove_spaces(row)\n",
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"lines.append( {'Speaker': remove_paren(speaker).strip(),\n",
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" 'Dialogue': remove_paren(dialogue).strip() } )\n",
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"\n",
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"roles = defaultdict(int)\n",
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"\n",
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"for line in lines:\n",
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" speaker = line['Speaker']\n",
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" roles[speaker] = roles[speaker] + 1\n",
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"\n",
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"toy_story_df = pd.DataFrame(lines[1:])\n",
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"toy_story_df.head()\n",
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"\n",
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"toy_story_df.Speaker.value_counts()\n",
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"\n",
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"def what_sex(speaker):\n",
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" if speaker in [\"SID'S MOM\", 'MRS. DAVIS', 'HANNAH', 'BO PEEP']:\n",
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" return 'Female'\n",
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" return 'Male'\n",
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"\n",
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"toy_story_df['Sex'] = toy_story_df['Speaker'].apply(what_sex)\n",
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"\n",
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"sex_df = toy_story_df.groupby('Sex').size()\n",
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"sex_df.plot(kind='bar')\n",
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"sex_df\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def word_count(dialogue):\n",
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" return len(dialogue.split())\n",
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"\n",
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"toy_story_df['Word Count'] = toy_story_df['Dialogue'].apply(word_count)\n",
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"\n",
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"word_df = toy_story_df.groupby('Sex')['Word Count'].sum()\n",
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"word_df.plot(kind='bar')\n",
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"word_df"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.1+"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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