lyrics_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from embedding import *\n",
    "from preprocessing import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/lyrics/music_hp_df.csv', index_col = 0)\n",
    "df.drop([4, 1958, 2299, 2433], inplace =True) #remove the 4 lyrics that are not found\n",
    "df.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['lyrics', 'year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('data/lyrics/spotify_songs.csv')\n",
    "df2= df2.loc[ (~df2.lyrics.isna()) & (df2.language == 'en')]\n",
    "df2['year'] = pd.to_datetime(df2['track_album_release_date']).dt.year\n",
    "df2 = df2[['lyrics', 'year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = pd.concat([df, df2])\n",
    "combined = combined[(combined['year'] >= 1980) & (combined['year']< 2020)].reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.to_csv('data/lyrics/80s_2010s_lyrics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count unique tokens, take a while to run\n",
    "tokenized = tokenize_lyric(combined.lyrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenized "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst= []\n",
    "for t in tokenized:\n",
    "    for word in t:\n",
    "        if word not in lst:\n",
    "            lst.append(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46241"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lst) #number of unique tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export csvs by decades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/lyrics/80s_2010s_lyrics.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year_bin'] = df.year.apply(convert_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010s    9864\n",
       "2000s    3271\n",
       "1990s    2248\n",
       "1980s    1638\n",
       "Name: year_bin, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.year_bin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1980s\n",
      "1990s\n",
      "2000s\n",
      "2010s\n"
     ]
    }
   ],
   "source": [
    "for decade in df.year_bin.unique():\n",
    "    print(decade)\n",
    "    decade_df = df.loc[df.year_bin == decade]\n",
    "    decade_df.to_csv('data/lyrics/{}_lyrics.csv'.format(decade))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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