{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hip hop over time\n",
"\n",
"Billboard has a \"R and B / Hip Hop\" list, which is a little absurd because the genres aren't *quite* the same. Let's track the changes over the years.\n",
"\n",
"And let's get this out of the way now: **every time you make a vectorizer**, you'll want to ask yourself a few questions.\n",
"\n",
"* What kind of vectorizer do you need? CountVectorizer? TfIdf?\n",
"* If TdIfd, do you use use_idf=True or use_idf=False?\n",
"* Does your vectorizer require a certain vocabulary?\n",
"* Do you care about multiple words (\"he said\" vs. \"she said\")? If so, do you need to do something special so it pays attention to that?\n",
"* Should you lemmatize/stem when you're processing?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reading in the files\n",
"\n",
"## Getting a list of every file we'll want to read in"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['hip-hop/1965/a-change-is-gonna-come-sam-cooke',\n",
" 'hip-hop/1965/a-lovers-concerto-the-toys',\n",
" 'hip-hop/1965/a-woman-can-change-a-man-joe-tex',\n",
" 'hip-hop/1965/a-womans-love-carla-thomas',\n",
" 'hip-hop/1965/aint-that-peculiar-marvin-gaye']"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import glob\n",
"filenames = glob.glob('hip-hop/*/*')\n",
"filenames[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading in the files using a list comprehension"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7458"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"contents = [open(filename).read() for filename in filenames]\n",
"len(contents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use the filenames and the contents to build a dataframe"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" filename | \n",
" lyrics | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" hip-hop/1965/a-change-is-gonna-come-sam-cooke | \n",
" [Verse 1]\\nI was born by the river\\nIn a littl... | \n",
"
\n",
" \n",
" 1 | \n",
" hip-hop/1965/a-lovers-concerto-the-toys | \n",
" How gentle is the rain\\nThat falls softly on t... | \n",
"
\n",
" \n",
" 2 | \n",
" hip-hop/1965/a-woman-can-change-a-man-joe-tex | \n",
" A man can say what he won't do\\nBut if she rea... | \n",
"
\n",
" \n",
" 3 | \n",
" hip-hop/1965/a-womans-love-carla-thomas | \n",
" When I ask you where you've been\\nDon't get an... | \n",
"
\n",
" \n",
" 4 | \n",
" hip-hop/1965/aint-that-peculiar-marvin-gaye | \n",
" [Verse 1]\\nHoney you do me wrong but still I'm... | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" filename \\\n",
"0 hip-hop/1965/a-change-is-gonna-come-sam-cooke \n",
"1 hip-hop/1965/a-lovers-concerto-the-toys \n",
"2 hip-hop/1965/a-woman-can-change-a-man-joe-tex \n",
"3 hip-hop/1965/a-womans-love-carla-thomas \n",
"4 hip-hop/1965/aint-that-peculiar-marvin-gaye \n",
"\n",
" lyrics \n",
"0 [Verse 1]\\nI was born by the river\\nIn a littl... \n",
"1 How gentle is the rain\\nThat falls softly on t... \n",
"2 A man can say what he won't do\\nBut if she rea... \n",
"3 When I ask you where you've been\\nDon't get an... \n",
"4 [Verse 1]\\nHoney you do me wrong but still I'm... "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame({\n",
" 'lyrics': contents,\n",
" 'filename': filenames\n",
"})\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract the year into a different column"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" filename | \n",
" lyrics | \n",
" year | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" hip-hop/1965/a-change-is-gonna-come-sam-cooke | \n",
" [Verse 1]\\nI was born by the river\\nIn a littl... | \n",
" 1965 | \n",
"
\n",
" \n",
" 1 | \n",
" hip-hop/1965/a-lovers-concerto-the-toys | \n",
" How gentle is the rain\\nThat falls softly on t... | \n",
" 1965 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" filename \\\n",
"0 hip-hop/1965/a-change-is-gonna-come-sam-cooke \n",
"1 hip-hop/1965/a-lovers-concerto-the-toys \n",
"\n",
" lyrics year \n",
"0 [Verse 1]\\nI was born by the river\\nIn a littl... 1965 \n",
"1 How gentle is the rain\\nThat falls softly on t... 1965 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# expand=False just gets rid of a warning\n",
"df['year'] = df.filename.str.extract('hip-hop/(\\d*)/', expand=False)\n",
"df.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use the year to create a datetime column\n",
"\n",
"Even though it's a lie, because the billboard charts are weekly. I just didn't save that information!"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" filename | \n",
" lyrics | \n",
" year | \n",
" datetime | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" hip-hop/1965/a-change-is-gonna-come-sam-cooke | \n",
" [Verse 1]\\nI was born by the river\\nIn a littl... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
"
\n",
" \n",
" 1 | \n",
" hip-hop/1965/a-lovers-concerto-the-toys | \n",
" How gentle is the rain\\nThat falls softly on t... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" filename \\\n",
"0 hip-hop/1965/a-change-is-gonna-come-sam-cooke \n",
"1 hip-hop/1965/a-lovers-concerto-the-toys \n",
"\n",
" lyrics year datetime \n",
"0 [Verse 1]\\nI was born by the river\\nIn a littl... 1965 1965-01-01 \n",
"1 How gentle is the rain\\nThat falls softly on t... 1965 1965-01-01 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['datetime'] = pd.to_datetime(df['year'], format=\"%Y\")\n",
"df.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract the artist and song name into another column"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" filename | \n",
" lyrics | \n",
" year | \n",
" datetime | \n",
" title-artist | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" hip-hop/1965/a-change-is-gonna-come-sam-cooke | \n",
" [Verse 1]\\nI was born by the river\\nIn a littl... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-change-is-gonna-come-sam-cooke | \n",
"
\n",
" \n",
" 1 | \n",
" hip-hop/1965/a-lovers-concerto-the-toys | \n",
" How gentle is the rain\\nThat falls softly on t... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-lovers-concerto-the-toys | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" filename \\\n",
"0 hip-hop/1965/a-change-is-gonna-come-sam-cooke \n",
"1 hip-hop/1965/a-lovers-concerto-the-toys \n",
"\n",
" lyrics year datetime \\\n",
"0 [Verse 1]\\nI was born by the river\\nIn a littl... 1965 1965-01-01 \n",
"1 How gentle is the rain\\nThat falls softly on t... 1965 1965-01-01 \n",
"\n",
" title-artist \n",
"0 a-change-is-gonna-come-sam-cooke \n",
"1 a-lovers-concerto-the-toys "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['title-artist'] = df.filename.str.extract('hip-hop/\\d*/(.*)', expand=False)\n",
"df.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning up a little more\n",
"\n",
"Let's get rid of things like `\"[Verse 1]\"` while we're at it."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" filename | \n",
" lyrics | \n",
" year | \n",
" datetime | \n",
" title-artist | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" hip-hop/1965/a-change-is-gonna-come-sam-cooke | \n",
" I was born by the river\\nIn a little tent\\nAnd... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-change-is-gonna-come-sam-cooke | \n",
"
\n",
" \n",
" 1 | \n",
" hip-hop/1965/a-lovers-concerto-the-toys | \n",
" How gentle is the rain\\nThat falls softly on t... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-lovers-concerto-the-toys | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" filename \\\n",
"0 hip-hop/1965/a-change-is-gonna-come-sam-cooke \n",
"1 hip-hop/1965/a-lovers-concerto-the-toys \n",
"\n",
" lyrics year datetime \\\n",
"0 I was born by the river\\nIn a little tent\\nAnd... 1965 1965-01-01 \n",
"1 How gentle is the rain\\nThat falls softly on t... 1965 1965-01-01 \n",
"\n",
" title-artist \n",
"0 a-change-is-gonna-come-sam-cooke \n",
"1 a-lovers-concerto-the-toys "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['lyrics'] = df['lyrics'].replace(\"\\[.*?\\]\", \"\", regex=True).str.strip()\n",
"df.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**OKAY!** We did it. We're done. it's clean. Let's get down to business.\n",
"\n",
"# Text analysis\n",
"\n",
"What do you want to do?"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" gin | \n",
" patron | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
"
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" \n",
" 2 | \n",
" 0 | \n",
" 0 | \n",
"
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" \n",
" 3 | \n",
" 0 | \n",
" 0 | \n",
"
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" \n",
" 4 | \n",
" 0 | \n",
" 0 | \n",
"
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" \n",
"
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"
"
],
"text/plain": [
" gin patron\n",
"0 0 0\n",
"1 0 0\n",
"2 0 0\n",
"3 0 0\n",
"4 0 0"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"# Make a new Count Vectorizer!!!!\n",
"# Let's only look for 'gin'\n",
"vec = CountVectorizer(vocabulary=['gin', 'patron'])\n",
"\n",
"# Say hey vectorizer, please read our stuff\n",
"matrix = vec.fit_transform(df['lyrics'])\n",
"\n",
"# And make a dataframe out of it\n",
"results = pd.DataFrame(matrix.toarray(), columns=vec.get_feature_names())\n",
"results.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" filename | \n",
" lyrics | \n",
" year | \n",
" datetime | \n",
" title-artist | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" hip-hop/1965/a-change-is-gonna-come-sam-cooke | \n",
" I was born by the river\\nIn a little tent\\nAnd... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-change-is-gonna-come-sam-cooke | \n",
"
\n",
" \n",
" 1 | \n",
" hip-hop/1965/a-lovers-concerto-the-toys | \n",
" How gentle is the rain\\nThat falls softly on t... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-lovers-concerto-the-toys | \n",
"
\n",
" \n",
" 2 | \n",
" hip-hop/1965/a-woman-can-change-a-man-joe-tex | \n",
" A man can say what he won't do\\nBut if she rea... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-woman-can-change-a-man-joe-tex | \n",
"
\n",
" \n",
"
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"
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"text/plain": [
" filename \\\n",
"0 hip-hop/1965/a-change-is-gonna-come-sam-cooke \n",
"1 hip-hop/1965/a-lovers-concerto-the-toys \n",
"2 hip-hop/1965/a-woman-can-change-a-man-joe-tex \n",
"\n",
" lyrics year datetime \\\n",
"0 I was born by the river\\nIn a little tent\\nAnd... 1965 1965-01-01 \n",
"1 How gentle is the rain\\nThat falls softly on t... 1965 1965-01-01 \n",
"2 A man can say what he won't do\\nBut if she rea... 1965 1965-01-01 \n",
"\n",
" title-artist \n",
"0 a-change-is-gonna-come-sam-cooke \n",
"1 a-lovers-concerto-the-toys \n",
"2 a-woman-can-change-a-man-joe-tex "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" filename | \n",
" lyrics | \n",
" year | \n",
" datetime | \n",
" title-artist | \n",
" gin | \n",
" patron | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" hip-hop/1965/a-change-is-gonna-come-sam-cooke | \n",
" I was born by the river\\nIn a little tent\\nAnd... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-change-is-gonna-come-sam-cooke | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
" hip-hop/1965/a-lovers-concerto-the-toys | \n",
" How gentle is the rain\\nThat falls softly on t... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-lovers-concerto-the-toys | \n",
" 0 | \n",
" 0 | \n",
"
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" \n",
" 2 | \n",
" hip-hop/1965/a-woman-can-change-a-man-joe-tex | \n",
" A man can say what he won't do\\nBut if she rea... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-woman-can-change-a-man-joe-tex | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" 3 | \n",
" hip-hop/1965/a-womans-love-carla-thomas | \n",
" When I ask you where you've been\\nDon't get an... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-womans-love-carla-thomas | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" 4 | \n",
" hip-hop/1965/aint-that-peculiar-marvin-gaye | \n",
" Honey you do me wrong but still I'm crazy abou... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" aint-that-peculiar-marvin-gaye | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
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],
"text/plain": [
" filename \\\n",
"0 hip-hop/1965/a-change-is-gonna-come-sam-cooke \n",
"1 hip-hop/1965/a-lovers-concerto-the-toys \n",
"2 hip-hop/1965/a-woman-can-change-a-man-joe-tex \n",
"3 hip-hop/1965/a-womans-love-carla-thomas \n",
"4 hip-hop/1965/aint-that-peculiar-marvin-gaye \n",
"\n",
" lyrics year datetime \\\n",
"0 I was born by the river\\nIn a little tent\\nAnd... 1965 1965-01-01 \n",
"1 How gentle is the rain\\nThat falls softly on t... 1965 1965-01-01 \n",
"2 A man can say what he won't do\\nBut if she rea... 1965 1965-01-01 \n",
"3 When I ask you where you've been\\nDon't get an... 1965 1965-01-01 \n",
"4 Honey you do me wrong but still I'm crazy abou... 1965 1965-01-01 \n",
"\n",
" title-artist gin patron \n",
"0 a-change-is-gonna-come-sam-cooke 0 0 \n",
"1 a-lovers-concerto-the-toys 0 0 \n",
"2 a-woman-can-change-a-man-joe-tex 0 0 \n",
"3 a-womans-love-carla-thomas 0 0 \n",
"4 aint-that-peculiar-marvin-gaye 0 0 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['gin'] = results['gin']\n",
"df['patron'] = results['patron']\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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1SYYjsi/PsNNycZKzJ2Y+OkNPs6dlOPp55CjYTZL89cTMO8U2afP4Vdsm2bdb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\nrkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.groupby('year')['gin'].sum().plot(kind='bar', figsize=(20,4))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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uTd3DfTDnhF5zktJlqapjM+w9vrqGM+DfLUO336l7Eg9Wp789yV/2umOxFzFW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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.groupby('year')['patron'].sum().plot(kind='bar', figsize=(20,4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ARE RAPPERS ANGRY?????"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read in the emotional lexicon"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" emotion | \n",
" word | \n",
" anger | \n",
" anticipation | \n",
" disgust | \n",
" fear | \n",
" joy | \n",
" negative | \n",
" positive | \n",
" sadness | \n",
" surprise | \n",
" trust | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" aback | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
" abacus | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
"
\n",
" \n",
" 2 | \n",
" abandon | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
"emotion word anger anticipation disgust fear joy negative positive \\\n",
"0 aback 0 0 0 0 0 0 0 \n",
"1 abacus 0 0 0 0 0 0 0 \n",
"2 abandon 0 0 0 1 0 1 0 \n",
"\n",
"emotion sadness surprise trust \n",
"0 0 0 0 \n",
"1 0 0 1 \n",
"2 1 0 0 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filepath = \"NRC-Emotion-Lexicon-v0.92/NRC-emotion-lexicon-wordlevel-alphabetized-v0.92.txt\"\n",
"emolex_df = pd.read_csv(filepath, names=[\"word\", \"emotion\", \"association\"], skiprows=45, sep='\\t')\n",
"emolex_df = emolex_df.pivot(index='word', columns='emotion', values='association').reset_index()\n",
"emolex_df.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pull out the words you want\n",
"\n",
"We want **angry** and **positive**"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"angry_words = emolex_df[emolex_df.anger == 1].word\n",
"positive_words = emolex_df[emolex_df.positive == 1].word"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the percentage of words that we have emotional ratings for"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" aback | \n",
" abacus | \n",
" abandon | \n",
" abandoned | \n",
" abandonment | \n",
" abate | \n",
" abatement | \n",
" abba | \n",
" abbot | \n",
" abbreviate | \n",
" ... | \n",
" zephyr | \n",
" zeppelin | \n",
" zest | \n",
" zip | \n",
" zodiac | \n",
" zone | \n",
" zoo | \n",
" zoological | \n",
" zoology | \n",
" zoom | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" ... | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" 1 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" ... | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
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\n",
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" 2 | \n",
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" 0.0 | \n",
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" ... | \n",
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\n",
" \n",
" 3 | \n",
" 0.0 | \n",
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" 0.0 | \n",
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" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" ... | \n",
" 0.0 | \n",
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" \n",
" 4 | \n",
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" 0.0 | \n",
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" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 14182 columns
\n",
"
"
],
"text/plain": [
" aback abacus abandon abandoned abandonment abate abatement abba \\\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" abbot abbreviate ... zephyr zeppelin zest zip zodiac zone zoo \\\n",
"0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" zoological zoology zoom \n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"\n",
"[5 rows x 14182 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"\n",
"# I only want you to look for words in the emotional lexicon\n",
"# because we don't know what's up with the other words\n",
"vec = TfidfVectorizer(vocabulary=emolex_df.word,\n",
" use_idf=False, \n",
" norm='l1') # ELL - ONE\n",
"matrix = vec.fit_transform(df['lyrics'])\n",
"vocab = vec.get_feature_names()\n",
"wordcount_df = pd.DataFrame(matrix.toarray(), columns=vocab)\n",
"wordcount_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add up the percent that are angry and the percent that are positive"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" filename | \n",
" lyrics | \n",
" year | \n",
" datetime | \n",
" title-artist | \n",
" gin | \n",
" patron | \n",
" anger | \n",
" positivity | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" hip-hop/1965/a-change-is-gonna-come-sam-cooke | \n",
" I was born by the river\\nIn a little tent\\nAnd... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-change-is-gonna-come-sam-cooke | \n",
" 0 | \n",
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" 0.000000 | \n",
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"
\n",
" \n",
" 1 | \n",
" hip-hop/1965/a-lovers-concerto-the-toys | \n",
" How gentle is the rain\\nThat falls softly on t... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-lovers-concerto-the-toys | \n",
" 0 | \n",
" 0 | \n",
" 0.022727 | \n",
" 0.409091 | \n",
"
\n",
" \n",
" 2 | \n",
" hip-hop/1965/a-woman-can-change-a-man-joe-tex | \n",
" A man can say what he won't do\\nBut if she rea... | \n",
" 1965 | \n",
" 1965-01-01 | \n",
" a-woman-can-change-a-man-joe-tex | \n",
" 0 | \n",
" 0 | \n",
" 0.000000 | \n",
" 0.090909 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" filename \\\n",
"0 hip-hop/1965/a-change-is-gonna-come-sam-cooke \n",
"1 hip-hop/1965/a-lovers-concerto-the-toys \n",
"2 hip-hop/1965/a-woman-can-change-a-man-joe-tex \n",
"\n",
" lyrics year datetime \\\n",
"0 I was born by the river\\nIn a little tent\\nAnd... 1965 1965-01-01 \n",
"1 How gentle is the rain\\nThat falls softly on t... 1965 1965-01-01 \n",
"2 A man can say what he won't do\\nBut if she rea... 1965 1965-01-01 \n",
"\n",
" title-artist gin patron anger positivity \n",
"0 a-change-is-gonna-come-sam-cooke 0 0 0.000000 0.142857 \n",
"1 a-lovers-concerto-the-toys 0 0 0.022727 0.409091 \n",
"2 a-woman-can-change-a-man-joe-tex 0 0 0.000000 0.090909 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['anger'] = wordcount_df[angry_words].sum(axis=1)\n",
"df['positivity'] = wordcount_df[positive_words].sum(axis=1)\n",
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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DALjrrrsmn2/fvh3bt2+f8QTnG6bKcYh//mdQda2mnxti/A7HAbwH4qR+/vnv4j//50uh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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(x='positivity', y='anger', kind='scatter')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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tVvU225TMM3s2HHeczRw58MDM22jUyGbW7L+/xXgZPTpczJjqxFZb2ec3aJCt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\ncXGvlowcCTNmQIsWNnuje3d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4IM+PqjoZ2JSguox/A5X9hZkhIscExwOwCwXYDUBExojI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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = df.groupby('year')['anger'].mean().plot()\n",
"df.groupby('year')['positivity'].mean().plot(ax=ax, c='red')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}