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govdoc-cr-analysis/text_analysis/readability.ipynb

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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
"import os \n",
"import textstat\n",
"import csv"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
"outputs": [],
"source": [
"contributing_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/contributing/\"\n",
"readme_directory = \"/data/users/mgaughan/kkex/012825_cam_revision_main/final_data/first_version_documents/readme/\"\n",
"\n",
"csv_fieldnames = [ 'filename', 'flesch_reading_ease', 'flesch_kincaid_grade', 'linsear_write_formula', 'dale_chall_readability_score', 'mcalpine_eflaw', 'reading_time', 'char_count', 'word_count']"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [],
"source": [
"def generate_file(output_csv, wdirectory):\n",
" with open(output_csv, 'w') as csvfile: \n",
" writer = csv.DictWriter(csvfile, fieldnames = csv_fieldnames) \n",
" writer.writeheader() \n",
" files = os.listdir(wdirectory)\n",
" for file in files:\n",
" file_dict = {\"filename\": file}\n",
" full_address = wdirectory + file\n",
" file_dict = get_readibility(full_address, file_dict)\n",
" writer.writerow(file_dict)\n"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [],
"source": [
"def get_readibility(file_address, file_dict):\n",
" file = open(file_address, \"r\", encoding='utf-8', errors=\"ignore\")\n",
" document = file.read()\n",
" file_dict['flesch_reading_ease'] = textstat.flesch_reading_ease(document)\n",
" file_dict['flesch_kincaid_grade'] = textstat.flesch_kincaid_grade(document)\n",
" file_dict['linsear_write_formula'] = textstat.linsear_write_formula(document)\n",
" file_dict['dale_chall_readability_score'] = textstat.dale_chall_readability_score(document)\n",
" file_dict['mcalpine_eflaw'] = textstat.mcalpine_eflaw(document)\n",
" file_dict['reading_time'] = textstat.reading_time(document, ms_per_char=14.69)\n",
" file_dict['char_count'] = textstat.char_count(document, ignore_spaces=True)\n",
" file_dict['word_count'] = textstat.lexicon_count(document, removepunct=True)\n",
" return file_dict"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"generate_file('020125_CONTRIBUTING_readability.csv', contributing_directory)"
]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [],
"source": [
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"generate_file('020325_README_readability.csv', readme_directory)"
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]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}