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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c6cadd34",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import glob"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4ce0c480",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filenames :\n",
"../../data/processed/uc-berkeley-measuring-hate-speech-negative-translated.csv\n",
"../../data/processed/caa_negative.csv\n",
"../../data/processed/caa_positive.csv\n",
"../../data/processed/ftr_dataset_negative.csv\n",
"../../data/processed/mlma_negative.csv\n",
"../../data/processed/merged_datasets.csv\n",
"../../data/processed/mlma_positive.csv\n",
"../../data/processed/ftr_dataset_positive.csv\n",
"../../data/processed/uc-berkeley-measuring-hate-speech-positive-translated.csv\n",
"\n",
"Columns:\n",
"['Unnamed: 2']\n",
"['tweet' 'sentiment' 'directness' 'annotator_sentiment' 'target' 'group']\n",
"['tweet' 'label' 'tweet_clean']\n",
"['translated' 'hate_speech_score' 'translated_clean' 'label']\n",
"['translated' 'hate_speech_score' 'translated_clean' 'label']\n",
"['Unnamed: 2']\n",
"['tweet' 'label' 'tweet_clean']\n",
"['tweet' 'sentiment' 'directness' 'annotator_sentiment' 'target' 'group']\n",
"['text']\n"
]
}
],
"source": [
"input_dir = \"../../data/processed/\"\n",
"output_dir = \"../../data/processed/\"\n",
"\n",
"filenames = glob.glob(input_dir + \"*\")\n",
"\n",
"print(\"Filenames :\")\n",
"pos, neg = [], []\n",
"for filename in filenames:\n",
" print(filename)\n",
" if \"positive\" in filename:\n",
" pos.append(pd.read_csv(filename, index_col=[0]))\n",
" else:\n",
" neg.append(pd.read_csv(filename, index_col=[0]))\n",
" \n",
"print()\n",
"print(\"Columns:\")\n",
"for pos_df in pos:\n",
" print(pos_df.columns.values)\n",
" \n",
"for neg_df in neg:\n",
" print(neg_df.columns.values)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ef7ee207",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Positive : 5011\n",
"Negative : 31378\n"
]
}
],
"source": [
"for pos_df in pos:\n",
" if \"Unnamed: 2\" in pos_df.columns:\n",
" pos_df.rename({\"Unnamed: 2\" : \"text\"}, inplace=True, axis=1)\n",
" elif \"tweet_clean\" in pos_df.columns:\n",
" pos_df.rename({\"tweet_clean\" : \"text\"}, inplace=True, axis=1)\n",
" elif 'translated_clean' in pos_df.columns:\n",
" pos_df.rename({\"translated_clean\" : \"text\"}, inplace=True, axis=1)\n",
" elif \"tweet\" in pos_df.columns:\n",
" pos_df.rename({\"tweet\" : \"text\"}, inplace=True, axis=1)\n",
" \n",
"for neg_df in neg:\n",
" if \"Unnamed: 2\" in neg_df.columns:\n",
" neg_df.rename({\"Unnamed: 2\" : \"text\"}, inplace=True, axis=1)\n",
" elif \"tweet_clean\" in neg_df.columns:\n",
" neg_df.rename({\"tweet_clean\" : \"text\"}, inplace=True, axis=1)\n",
" elif 'translated_clean' in neg_df.columns:\n",
" neg_df.rename({\"translated_clean\" : \"text\"}, inplace=True, axis=1)\n",
" elif \"tweet\" in neg_df.columns:\n",
" neg_df.rename({\"tweet\" : \"text\"}, inplace=True, axis=1)\n",
" \n",
"pos = pd.concat(pos)[[\"text\"]]\n",
"pos[\"label\"] = 1\n",
"neg = pd.concat(neg)[[\"text\"]]\n",
"neg[\"label\"] = 0\n",
"\n",
"print(\"Positive :\", pos.shape[0])\n",
"print(\"Negative :\", neg.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3ec5fa31",
"metadata": {},
"outputs": [],
"source": [
"merged = pd.concat([pos, neg])\n",
"merged.to_csv(os.path.join(output_dir, \"merged_datasets.csv\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fe7d6ab",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd896f4c",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "sexism_detection",
"language": "python",
"name": "sexism_detection"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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