{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using topic modeling to extract topics from documents\n",
"\n",
"Sometimes you have a nice big set of documents, and all you wish for is to know what's hiding inside. But without reading them, of course! Two approaches to try to lazily get some information from your texts are **topic modeling** and **clustering**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p class=\"reading-options\">\n <a class=\"btn\" href=\"/text-analysis/introduction-to-topic-modeling\">\n <i class=\"fa fa-sm fa-book\"></i>\n Read online\n </a>\n <a class=\"btn\" href=\"/text-analysis/notebooks/Introduction to topic modeling.ipynb\">\n <i class=\"fa fa-sm fa-download\"></i>\n Download notebook\n </a>\n <a class=\"btn\" href=\"https://colab.research.google.com/github/littlecolumns/ds4j-notebooks/blob/master/text-analysis/notebooks/Introduction to topic modeling.ipynb\" target=\"_new\">\n <i class=\"fa fa-sm fa-laptop\"></i>\n Interactive version\n </a>\n</p>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prep work: Downloading necessary files\n",
"Before we get started, we need to download all of the data we'll be using.\n",
"* **recipes.csv:** recipes - a list of recipes (but only with ingredient names)\n",
"* **state-of-the-union.csv:** State of the Union addresses - each presidential address from 1970 to 2012\n"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# Make data directory if it doesn't exist\n",
"!mkdir -p data\n",
"!wget -nc https://nyc3.digitaloceanspaces.com/ml-files-distro/v1/text-analysis/data/recipes.csv -P data\n",
"!wget -nc https://nyc3.digitaloceanspaces.com/ml-files-distro/v1/text-analysis/data/state-of-the-union.csv -P data"
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How computers read\n",
"\n",
"I'm going to tell you a big secret: **computers are really really really bad at reading documents and figuring out what they're about.** Text is for _people_ to read, people with a collective knowledge of The World At Large and a history of reading things and all kinds of other tricky secret little things we don't think about that help us understand what a piece of text means.\n",
"\n",
"When dealing with understanding content, computers are good for _very specific situations_ to do _very specific things_. Or alternatively, to do a not-that-great job when you aren't going to be terribly picky about the results.\n",
"\n",
"Do I sound a little biased? Oh, but aren't we all. It isn't going to stop us from talking about it, though!\n",
"\n",
"Before we start, **let's make some assumptions:**\n",
"\n",
"* When you're dealing with documents, each document is (typically) about something.\n",
"* You know each document is about by looking at the words in the document.\n",
"* Documents with similar words are probably about similar things. \n",
"\n",
"We have two major options available to us: **topic modeling** and **clustering**. There's a lot of NLP nuance going on between the two, but we're going to keep it simple:\n",
"\n",
"**Topic modeling** is if each document can be about **multiple topics**. There might be 100 different topics, and a document might be 30% about one topic, 20% about another, and then 50% spread out between the others.\n",
"\n",
"**Clustering** is if each document should only fit into **one topic**. It's an all-or-nothing approach.\n",
"\n",
"The most important part of _all of this_ is the fact that **the computer figures out these topics by itself**. You don't tell it what to do! If you're teaching the algorithm what different specific topics look like, that's **classification.** In this case we're just saying \"hey computer, please figure this out!\"\n",
"\n",
"Let's get started."
]
},
{
"cell_type": "code",
"execution_count": 316,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# These styles look nicer than default pandas\n",
"plt.style.use('ggplot')\n",
"\n",
"# We'll be able to see more text at once\n",
"pd.set_option(\"display.max_colwidth\", 100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Attempt one: Recipes\n",
"\n",
"### Our dataset\n",
"\n",
"We're going to start with analyzing **about 36,000 recipes**. Food is interesting because you can split it so many ways: by courses, or by baked goods vs meat vs vegetables vs others, by national cuisine..."
]
},
{
"cell_type": "code",
"execution_count": 317,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cuisine</th>\n",
" <th>id</th>\n",
" <th>ingredient_list</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>greek</td>\n",
" <td>10259</td>\n",
" <td>romaine lettuce, black olives, grape tomatoes, garlic, pepper, purple onion, seasoning, garbanzo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>southern_us</td>\n",
" <td>25693</td>\n",
" <td>plain flour, ground pepper, salt, tomatoes, ground black pepper, thyme, eggs, green tomatoes, ye...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>filipino</td>\n",
" <td>20130</td>\n",
" <td>eggs, pepper, salt, mayonaise, cooking oil, green chilies, grilled chicken breasts, garlic powde...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>indian</td>\n",
" <td>22213</td>\n",
" <td>water, vegetable oil, wheat, salt</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>indian</td>\n",
" <td>13162</td>\n",
" <td>black pepper, shallots, cornflour, cayenne pepper, onions, garlic paste, milk, butter, salt, lem...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cuisine id \\\n",
"0 greek 10259 \n",
"1 southern_us 25693 \n",
"2 filipino 20130 \n",
"3 indian 22213 \n",
"4 indian 13162 \n",
"\n",
" ingredient_list \n",
"0 romaine lettuce, black olives, grape tomatoes, garlic, pepper, purple onion, seasoning, garbanzo... \n",
"1 plain flour, ground pepper, salt, tomatoes, ground black pepper, thyme, eggs, green tomatoes, ye... \n",
"2 eggs, pepper, salt, mayonaise, cooking oil, green chilies, grilled chicken breasts, garlic powde... \n",
"3 water, vegetable oil, wheat, salt \n",
"4 black pepper, shallots, cornflour, cayenne pepper, onions, garlic paste, milk, butter, salt, lem... "
]
},
"execution_count": 317,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"recipes = pd.read_csv(\"data/recipes.csv\")\n",
"recipes.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to analyze the text, we'll need to count the words in each recipe. To do that we're going to use a **stemmed TF-IDF vectorizer** from scikit-learn.\n",
"\n",
"* **Stemming** will allow us to combine words like `tomato` and `tomatoes`\n",
"* Using **TF-IDF** will allow us to devalue common ingredients like salt and water\n",
"\n",
"I'm using the code from [the reference section](https://investigate.ai/reference/vectorizing/#stem-and-vectorize), just adjusted from a `CountVectorizer` to a `TfidfVectorizer`, and set it so ingredients have to appear in at least **fifty recipes**."
]
},
{
"cell_type": "code",
"execution_count": 318,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"import Stemmer\n",
"\n",
"# English stemmer from pyStemmer\n",
"stemmer = Stemmer.Stemmer('en')\n",
"\n",
"analyzer = TfidfVectorizer().build_analyzer()\n",
"\n",
"# Override TfidfVectorizer\n",
"class StemmedTfidfVectorizer(TfidfVectorizer):\n",
" def build_analyzer(self):\n",
" analyzer = super(TfidfVectorizer, self).build_analyzer()\n",
" return lambda doc: stemmer.stemWords(analyzer(doc))"
]
},
{
"cell_type": "code",
"execution_count": 319,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>activ</th>\n",
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" <th>anaheim</th>\n",
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"<p>5 rows \u00d7 752 columns</p>\n",
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"text/plain": [
" activ adobo agav alfredo all allspic almond amchur anaheim ancho \\\n",
"0 0.0 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 0.0 \n",
"2 0.0 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 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" ... wrapper yam yeast yellow yoghurt yogurt yolk yukon zest \\\n",
"0 ... 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 \n",
"1 ... 0.0 0.0 0.0 0.278745 0.0 0.000000 0.0 0.0 0.0 \n",
"2 ... 0.0 0.0 0.0 0.276000 0.0 0.000000 0.0 0.0 0.0 \n",
"3 ... 0.0 0.0 0.0 0.000000 0.0 0.000000 0.0 0.0 0.0 \n",
"4 ... 0.0 0.0 0.0 0.000000 0.0 0.210575 0.0 0.0 0.0 \n",
"\n",
" zucchini \n",
"0 0.0 \n",
"1 0.0 \n",
"2 0.0 \n",
"3 0.0 \n",
"4 0.0 \n",
"\n",
"[5 rows x 752 columns]"
]
},
"execution_count": 319,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vectorizer = StemmedTfidfVectorizer(min_df=50)\n",
"matrix = vectorizer.fit_transform(recipes.ingredient_list)\n",
"\n",
"words_df = pd.DataFrame(matrix.toarray(),\n",
" columns=vectorizer.get_feature_names())\n",
"words_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like we have 752 ingredients! Yes, there are some numbers in there and probably other things we aren't interested in, but let's stick with it for now."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Topic modeling\n",
"\n",
"There are multiple techniques for topic modeling, but in the end they do the same thing: **you get a list of topics, and a list of words associated with each topic.**\n",
"\n",
"Let's tell it to break them down into **five topics.**"
]
},
{
"cell_type": "code",
"execution_count": 320,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,\n",
" n_components=5, random_state=None, shuffle=False, solver='cd', tol=0.0001,\n",
" verbose=0)"
]
},
"execution_count": 320,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.decomposition import NMF\n",
"\n",
"model = NMF(n_components=5)\n",
"model.fit(matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why five topics? **Because we have to tell it _something_.** Our job is to decide the number of topics, and it's the computer's job to find the topics. We'll talk about how to pick the \"right\" number later, but for now: it's magic.\n",
"\n",
"Fitting the model allowed it to \"learn\" what the ingredients are and how they're organized, we just need to find out what's inside. Let's ask for the **top ten terms in each group.**"
]
},
{
"cell_type": "code",
"execution_count": 321,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Topic 0: oliv pepper fresh oil dri garlic salt parsley red tomato\n",
"Topic 1: flour egg sugar purpos all butter bake milk larg powder\n",
"Topic 2: sauc soy sesam rice oil ginger sugar chicken vinegar garlic\n",
"Topic 3: ground chili cilantro cumin powder lime onion pepper chop fresh\n",
"Topic 4: chees shred cream parmesan cheddar grate tortilla mozzarella sour chicken\n"
]
}
],
"source": [
"n_words = 10\n",
"feature_names = vectorizer.get_feature_names()\n",
"\n",
"topic_list = []\n",
"for topic_idx, topic in enumerate(model.components_):\n",
" top_features = [feature_names[i] for i in topic.argsort()][::-1][:n_words]\n",
" top_n = ' '.join(top_features)\n",
" topic_list.append(f\"topic_{'_'.join(top_features[:3])}\") \n",
"\n",
" print(f\"Topic {topic_idx}: {top_n}\")"
]
},
{
"cell_type": "code",
"execution_count": 322,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['topic_oliv_pepper_fresh', 'topic_flour_egg_sugar', 'topic_sauc_soy_sesam', 'topic_ground_chili_cilantro', 'topic_chees_shred_cream']\n"
]
}
],
"source": [
"print(topic_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Those actually seem like _pretty good topics_. Italian-ish, then baking, then Chinese, maybe Latin American or Indian food, and then dairy. What if we did it with **fifteen topics** instead?"
]
},
{
"cell_type": "code",
"execution_count": 323,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Topic 0: pepper bell red green onion celeri flake black tomato crush\n",
"Topic 1: flour purpos all bake powder butter soda buttermilk salt egg\n",
"Topic 2: sauc soy sesam oil ginger rice sugar garlic scallion starch\n",
"Topic 3: tortilla cream shred chees sour cheddar salsa corn bean jack\n",
"Topic 4: chees parmesan grate mozzarella pasta ricotta basil italian fresh spinach\n",
"Topic 5: lime cilantro fresh chop juic jalapeno chile avocado chili fish\n",
"Topic 6: chicken breast boneless skinless broth halv sodium low fat thigh\n",
"Topic 7: ground black pepper cumin cinnamon salt beef cayenn kosher paprika\n",
"Topic 8: chili seed powder cumin coriand masala garam curri ginger coconut\n",
"Topic 9: sugar egg vanilla milk extract larg cream butter yolk unsalt\n",
"Topic 10: oliv extra virgin oil clove garlic fresh salt tomato parsley\n",
"Topic 11: white wine vinegar rice shallot red salt grain mustard sugar\n",
"Topic 12: dri oregano tomato thyme parsley garlic bay basil leaf onion\n",
"Topic 13: lemon juic fresh orang zest parsley grate mint peel yogurt\n",
"Topic 14: water yeast warm sugar salt cold flour activ boil ice\n"
]
}
],
"source": [
"model = NMF(n_components=15)\n",
"model.fit(matrix)\n",
"\n",
"n_words = 10\n",
"feature_names = vectorizer.get_feature_names()\n",
"\n",
"topic_list = []\n",
"for topic_idx, topic in enumerate(model.components_):\n",
" top_n = [feature_names[i]\n",
" for i in topic.argsort()\n",
" [-n_words:]][::-1]\n",
" top_features = ' '.join(top_n)\n",
" topic_list.append(f\"topic_{'_'.join(top_n[:3])}\") \n",
"\n",
" print(f\"Topic {topic_idx}: {top_features}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is where we start to see **the big difference between categories and topics**. The grouping with five groups seemed very much like cuisines - Italian, Chinese, etc. But now that we're breaking it down further, the groups have changed a bit.\n",
"\n",
"They're now **more like classes of ingredients.** Baking gets a category - `chicken breast boneless skinless` and so do generic Mediterranean ingredients - `oliv extra virgin oil clove garlic fresh salt`. The algorithm got a little confused about black pepper vs. hot pepper flakes vs green/yellow bell peppers when it created `pepper bell red green onion celeri flake black`, but we understand what it's going for.\n",
"\n",
"Remember, the important thing about topic modeling is that every row in our dataset is a **combinations of topics**. It might be a little bit about one thing, a little bit less about another, etc etc. Let's take a look at how that works."
]
},
{
"cell_type": "code",
"execution_count": 324,
"metadata": {},
"outputs": [
{
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" <th>topic_sauc_soy_sesam</th>\n",
" <th>topic_tortilla_cream_shred</th>\n",
" <th>topic_chees_parmesan_grate</th>\n",
" <th>topic_lime_cilantro_fresh</th>\n",
" <th>topic_chicken_breast_boneless</th>\n",
" <th>topic_ground_black_pepper</th>\n",
" <th>topic_chili_seed_powder</th>\n",
" <th>topic_sugar_egg_vanilla</th>\n",
" <th>topic_oliv_extra_virgin</th>\n",
" <th>topic_white_wine_vinegar</th>\n",
" <th>topic_dri_oregano_tomato</th>\n",
" <th>topic_lemon_juic_fresh</th>\n",
" <th>topic_water_yeast_warm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.438751</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.671147</td>\n",
" <td>1.613176</td>\n",
" <td>0.2249</td>\n",
" <td>0.0</td>\n",
" <td>0.507867</td>\n",
" <td>0.0000</td>\n",
" <td>0.00000</td>\n",
" <td>2.561140</td>\n",
" <td>0.0</td>\n",
" <td>0.472827</td>\n",
" <td>0.0</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.447803</td>\n",
" <td>3.044097</td>\n",
" <td>0.176603</td>\n",
" <td>1.411024</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0</td>\n",
" <td>5.717447</td>\n",
" <td>0.4958</td>\n",
" <td>1.59881</td>\n",
" <td>0.548627</td>\n",
" <td>0.0</td>\n",
" <td>1.592754</td>\n",
" <td>0.0</td>\n",
" <td>0.2495</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" topic_pepper_bell_red topic_flour_purpos_all topic_sauc_soy_sesam \\\n",
"0 1.438751 0.000000 0.000000 \n",
"1 2.447803 3.044097 0.176603 \n",
"\n",
" topic_tortilla_cream_shred topic_chees_parmesan_grate \\\n",
"0 2.671147 1.613176 \n",
"1 1.411024 0.000000 \n",
"\n",
" topic_lime_cilantro_fresh topic_chicken_breast_boneless \\\n",
"0 0.2249 0.0 \n",
"1 0.0000 0.0 \n",
"\n",
" topic_ground_black_pepper topic_chili_seed_powder \\\n",
"0 0.507867 0.0000 \n",
"1 5.717447 0.4958 \n",
"\n",
" topic_sugar_egg_vanilla topic_oliv_extra_virgin topic_white_wine_vinegar \\\n",
"0 0.00000 2.561140 0.0 \n",
"1 1.59881 0.548627 0.0 \n",
"\n",
" topic_dri_oregano_tomato topic_lemon_juic_fresh topic_water_yeast_warm \n",
"0 0.472827 0.0 0.0000 \n",
"1 1.592754 0.0 0.2495 "
]
},
"execution_count": 324,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# If we don't want 'real' names for the topics, we can run this line\n",
"# topic_list = [f\"topic_{i}\" for i in range(model.n_components_)]\n",
"\n",
"# Convert our counts into numbers\n",
"amounts = model.transform(matrix) * 100\n",
"\n",
"# Set it up as a dataframe\n",
"topics = pd.DataFrame(amounts, columns=topic_list)\n",
"topics.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our first recipe is primary `topic_3` with a rating of 2.44, but it's also a bit topic 0 and topic 8 with scores of 1.5 and 1.36.\n",
"\n",
"Our second recipe is a bit bolder - it scores a whopping 5.7 in `topic_7`, with 0, 8 and 14 coming up in the 2.5-3 range.\n",
"\n",
"Let's combine this topics dataframe with our **original dataframe** so we can see it all in one place."
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cuisine</th>\n",
" <th>id</th>\n",
" <th>ingredient_list</th>\n",
" <th>topic_pepper_bell_red</th>\n",
" <th>topic_flour_purpos_all</th>\n",
" <th>topic_sauc_soy_sesam</th>\n",
" <th>topic_tortilla_cream_shred</th>\n",
" <th>topic_chees_parmesan_grate</th>\n",
" <th>topic_lime_cilantro_fresh</th>\n",
" <th>topic_chicken_breast_boneless</th>\n",
" <th>topic_ground_black_pepper</th>\n",
" <th>topic_chili_seed_powder</th>\n",
" <th>topic_sugar_egg_vanilla</th>\n",
" <th>topic_oliv_extra_virgin</th>\n",
" <th>topic_white_wine_vinegar</th>\n",
" <th>topic_dri_oregano_tomato</th>\n",
" <th>topic_lemon_juic_fresh</th>\n",
" <th>topic_water_yeast_warm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>greek</td>\n",
" <td>10259</td>\n",
" <td>romaine lettuce, black olives, grape tomatoes, garlic, pepper, purple onion, seasoning, garbanzo...</td>\n",
" <td>1.438751</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.671147</td>\n",
" <td>1.613176</td>\n",
" <td>0.2249</td>\n",
" <td>0.0</td>\n",
" <td>0.507867</td>\n",
" <td>0.0000</td>\n",
" <td>0.00000</td>\n",
" <td>2.561140</td>\n",
" <td>0.0</td>\n",
" <td>0.472827</td>\n",
" <td>0.0</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>southern_us</td>\n",
" <td>25693</td>\n",
" <td>plain flour, ground pepper, salt, tomatoes, ground black pepper, thyme, eggs, green tomatoes, ye...</td>\n",
" <td>2.447803</td>\n",
" <td>3.044097</td>\n",
" <td>0.176603</td>\n",
" <td>1.411024</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0</td>\n",
" <td>5.717447</td>\n",
" <td>0.4958</td>\n",
" <td>1.59881</td>\n",
" <td>0.548627</td>\n",
" <td>0.0</td>\n",
" <td>1.592754</td>\n",
" <td>0.0</td>\n",
" <td>0.2495</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cuisine id \\\n",
"0 greek 10259 \n",
"1 southern_us 25693 \n",
"\n",
" ingredient_list \\\n",
"0 romaine lettuce, black olives, grape tomatoes, garlic, pepper, purple onion, seasoning, garbanzo... \n",
"1 plain flour, ground pepper, salt, tomatoes, ground black pepper, thyme, eggs, green tomatoes, ye... \n",
"\n",
" topic_pepper_bell_red topic_flour_purpos_all topic_sauc_soy_sesam \\\n",
"0 1.438751 0.000000 0.000000 \n",
"1 2.447803 3.044097 0.176603 \n",
"\n",
" topic_tortilla_cream_shred topic_chees_parmesan_grate \\\n",
"0 2.671147 1.613176 \n",
"1 1.411024 0.000000 \n",
"\n",
" topic_lime_cilantro_fresh topic_chicken_breast_boneless \\\n",
"0 0.2249 0.0 \n",
"1 0.0000 0.0 \n",
"\n",
" topic_ground_black_pepper topic_chili_seed_powder \\\n",
"0 0.507867 0.0000 \n",
"1 5.717447 0.4958 \n",
"\n",
" topic_sugar_egg_vanilla topic_oliv_extra_virgin topic_white_wine_vinegar \\\n",
"0 0.00000 2.561140 0.0 \n",
"1 1.59881 0.548627 0.0 \n",
"\n",
" topic_dri_oregano_tomato topic_lemon_juic_fresh topic_water_yeast_warm \n",
"0 0.472827 0.0 0.0000 \n",
"1 1.592754 0.0 0.2495 "
]
},
"execution_count": 325,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged = recipes.merge(topics, right_index=True, left_index=True)\n",
"merged.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can do things like...\n",
"\n",
"* Uncover possible topics discussed in the dataset\n",
"* See how many documents cover each topic\n",
"* Find the top documents in each topic\n",
"\n",
"And **graph it!** Let's see what our distribution of topics looks like."
]
},
{
"cell_type": "code",
"execution_count": 340,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x16c0f15f8>"
]
},
"execution_count": 340,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = merged[topic_list].sum().to_frame().T.plot(kind='barh', stacked=True)\n",
"\n",
"# Move the legend off of the chart\n",
"ax.legend(loc=(1.04,0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Suspiciously even, but that's an investigation for another day. Let's try a different dataset that splits a little differently."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Attempt two: State of the Union addresses\n",
"\n",
"One of the fun things to do with topic modeling is see how **things change over time.** For this example, we're going to reproduce an assignment from [Jonathan Stray's Computational Journalism course](http://www.compjournalism.com/?p=208).\n",
"\n",
"At the beginning of each year, the President of the United States traditionally addresses Congress in a speech called the State of the Union. It's a good way to judge what's important in the country at the time, because the speech is sure to be used as a platform to address the legislative agenda for the year. Let's see if topic modeling can help illustrate how it's **changed over time.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Our data\n",
"\n",
"We have a simple CSV of State of the Union addresses, nothing too crazy."
]
},
{
"cell_type": "code",
"execution_count": 357,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>content</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>1849</td>\n",
" <td>\\nState of the Union Address\\nZachary Taylor\\nDecember 4, 1849\\n\\nFellow-Citizens of the Senate ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>1883</td>\n",
" <td>\\nState of the Union Address\\nChester A. Arthur\\nDecember 4, 1883\\n\\nTo the Congress of the Unit...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>1852</td>\n",
" <td>\\nState of the Union Address\\nMillard Fillmore\\nDecember 6, 1852\\n\\nFellow-Citizens of the Senat...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>1938</td>\n",
" <td>\\nState of the Union Address\\nFranklin D. Roosevelt\\nJanuary 3, 1938\\n\\nMr. President, Mr. Speak...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>156</th>\n",
" <td>1946</td>\n",
" <td>\\nState of the Union Address\\nHarry S. Truman\\nJanuary 21, 1946\\n\\nTo the Congress of the United...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year \\\n",
"60 1849 \n",
"94 1883 \n",
"63 1852 \n",
"148 1938 \n",
"156 1946 \n",
"\n",
" content \n",
"60 \\nState of the Union Address\\nZachary Taylor\\nDecember 4, 1849\\n\\nFellow-Citizens of the Senate ... \n",
"94 \\nState of the Union Address\\nChester A. Arthur\\nDecember 4, 1883\\n\\nTo the Congress of the Unit... \n",
"63 \\nState of the Union Address\\nMillard Fillmore\\nDecember 6, 1852\\n\\nFellow-Citizens of the Senat... \n",
"148 \\nState of the Union Address\\nFranklin D. Roosevelt\\nJanuary 3, 1938\\n\\nMr. President, Mr. Speak... \n",
"156 \\nState of the Union Address\\nHarry S. Truman\\nJanuary 21, 1946\\n\\nTo the Congress of the United... "
]
},
"execution_count": 357,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"speeches = pd.read_csv(\"data/state-of-the-union.csv\")\n",
"speeches.sample(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's not too many, only a little over 226. Because it's a smaller dataset, we're able to do more computationally intensive forms of topic modeling (LDA, for example) without sitting around getting bored."
]
},
{
"cell_type": "code",
"execution_count": 358,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(226, 2)"
]
},
"execution_count": 358,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"speeches.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To help the analysis out a bit, we're going to clean the text. Only a little bit, though - we'll just remove anything that isn't a word."
]
},
{
"cell_type": "code",
"execution_count": 359,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>content</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1790</td>\n",
" <td>George WashingtonJanuary Fellow Citizens of the Senate and House of Representatives I emb...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1790</td>\n",
" <td>State of the Union AddressGeorge WashingtonDecember Fellow Citizens of the Senate and Hou...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1791</td>\n",
" <td>State of the Union AddressGeorge WashingtonOctober Fellow Citizens of the Senate and Hou...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1792</td>\n",
" <td>State of the Union AddressGeorge WashingtonNovember Fellow Citizens of the Senate and Hou...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1793</td>\n",
" <td>State of the Union AddressGeorge WashingtonDecember Fellow Citizens of the Senate and Hou...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year \\\n",
"0 1790 \n",
"1 1790 \n",
"2 1791 \n",
"3 1792 \n",
"4 1793 \n",
"\n",
" content \n",
"0 George WashingtonJanuary Fellow Citizens of the Senate and House of Representatives I emb... \n",
"1 State of the Union AddressGeorge WashingtonDecember Fellow Citizens of the Senate and Hou... \n",
"2 State of the Union AddressGeorge WashingtonOctober Fellow Citizens of the Senate and Hou... \n",
"3 State of the Union AddressGeorge WashingtonNovember Fellow Citizens of the Senate and Hou... \n",
"4 State of the Union AddressGeorge WashingtonDecember Fellow Citizens of the Senate and Hou... "
]
},
"execution_count": 359,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove non-word characters, so numbers and ___ etc\n",
"speeches.content = speeches.content.str.replace(\"[^A-Za-z ]\", \" \")\n",
"speeches.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vectorize\n",
"\n",
"We're going to use the same TF-IDF vectorizer we used up above, which stems in addition to just vectorizing. We'll reproduce the code down here for completeness's sake (and easy cut-and-paste)."
]
},
{
"cell_type": "code",
"execution_count": 360,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"import Stemmer\n",
"\n",
"# English stemmer from pyStemmer\n",
"stemmer = Stemmer.Stemmer('en')\n",
"\n",
"analyzer = TfidfVectorizer().build_analyzer()\n",
"\n",
"# Override TfidfVectorizer\n",
"class StemmedTfidfVectorizer(TfidfVectorizer):\n",
" def build_analyzer(self):\n",
" analyzer = super(TfidfVectorizer, self).build_analyzer()\n",
" return lambda doc: stemmer.stemWords(analyzer(doc))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With our first pass we'll **vectorize everything**, no limits!"
]
},
{
"cell_type": "code",
"execution_count": 361,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>aaa</th>\n",
" <th>aana</th>\n",
" <th>aaron</th>\n",
" <th>abail</th>\n",
" <th>abal</th>\n",
" <th>abalanc</th>\n",
" <th>abandon</th>\n",
" <th>abandonedbi</th>\n",
" <th>abandonednow</th>\n",
" <th>abandonedtheir</th>\n",
" <th>...</th>\n",
" <th>zimbabwean</th>\n",
" <th>zinc</th>\n",
" <th>zionchurch</th>\n",
" <th>zollverein</th>\n",
" <th>zone</th>\n",
" <th>zoneof</th>\n",
" <th>zonesin</th>\n",
" <th>zoolog</th>\n",
" <th>zoom</th>\n",
" <th>zuloaga</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <th>3</th>\n",
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"</table>\n",
"<p>5 rows \u00d7 73222 columns</p>\n",
"</div>"
],
"text/plain": [
" aaa aana aaron abail abal abalanc abandon abandonedbi abandonednow \\\n",
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" abandonedtheir ... zimbabwean zinc zionchurch zollverein zone \\\n",
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"[5 rows x 73222 columns]"
]
},
"execution_count": 361,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vectorizer = StemmedTfidfVectorizer(stop_words='english')\n",
"matrix = vectorizer.fit_transform(speeches.content)\n",
"\n",
"words_df = pd.DataFrame(matrix.toarray(),\n",
" columns=vectorizer.get_feature_names())\n",
"words_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Running NME/NMF topic modeling\n",
"\n",
"Now we'll leap into topic modeling. We'll look at fifteen topics, since we're covering a long span of time where lots of different things may have happened."
]
},
{
"cell_type": "code",
"execution_count": 362,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Topic 0: state govern mexico unit congress constitut territori power countri act\n",
"Topic 1: program year nation billion new feder congress help american goal\n",
"Topic 2: america tonight year ve american job work let peopl help\n",
"Topic 3: war enemi fight victori nation japanes hitler american forc men\n",
"Topic 4: law nation govern state work corpor men great man peopl\n",
"Topic 5: state unit nation public govern congress year great commerc million\n",
"Topic 6: terrorist iraq america iraqi terror tonight american al help regim\n",
"Topic 7: soviet world nation free defens communist peac econom militari freedom\n",
"Topic 8: govern state bank public subject countri duti peopl treasuri general\n",
"Topic 9: year state govern unit report law congress cent silver increas\n",
"Topic 10: govern state unit treati countri congress american relat convent spain\n",
"Topic 11: nation world govern peopl democraci today problem state peac congress\n",
"Topic 12: gentlemen state unit nation public law measur indian object repres\n",
"Topic 13: shall matter men govern peopl countri thought great make necessari\n",
"Topic 14: govern agricultur year countri feder nation public industri econom bank\n"
]
}
],
"source": [
"model = NMF(n_components=15)\n",
"model.fit(matrix)\n",
"\n",
"n_words = 10\n",
"feature_names = vectorizer.get_feature_names()\n",
"\n",
"topic_list = []\n",
"for topic_idx, topic in enumerate(model.components_):\n",
" top_n = [feature_names[i]\n",
" for i in topic.argsort()\n",
" [-n_words:]][::-1]\n",
" top_features = ' '.join(top_n)\n",
" topic_list.append(f\"topic_{'_'.join(top_n[:3])}\") \n",
"\n",
" print(f\"Topic {topic_idx}: {top_features}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's be honest with ourselves: **we expected something a bit better.** So many of these words are so _common_ that it doesn't do much to convince me these are meaningful concepts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adjusting our min and max document frequency\n",
"\n",
"One way to cut those overly broad topics from our topic model is to **remove them from the vectorizer.** Instead of accepting _all_ words, we can set minimum or maximum limits.\n",
"\n",
"Let's only accept words used **in at least 5 speeches**, but also **don't appear in more than half of the speeches**."
]
},
{
"cell_type": "code",
"execution_count": 363,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" abal abalanc abandon abat abdic abett abey abhorr abid abil \\\n",
"0 0.0 0.0 0.0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 \n",
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"\n",
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"[5 rows x 8272 columns]"
]
},
"execution_count": 363,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vectorizer = StemmedTfidfVectorizer(stop_words='english', min_df=5, max_df=0.5)\n",
"matrix = vectorizer.fit_transform(speeches.content)\n",
"\n",
"words_df = pd.DataFrame(matrix.toarray(),\n",
" columns=vectorizer.get_feature_names())\n",
"words_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we'll **check the topic model.**"
]
},
{
"cell_type": "code",
"execution_count": 364,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Topic 0: island cuba arbitr june spain pension award confer consular commission\n",
"Topic 1: program billion today goal budget achiev area level farm percent\n",
"Topic 2: ve job tonight budget cut ll school spend don deficit\n",
"Topic 3: spain coloni franc articl intercours tribe minist port navig commenc\n",
"Topic 4: terrorist iraq iraqi terror tonight al regim afghanistan qaeda fight\n",
"Topic 5: fight enemi japanes today democraci victori tank plane task attack\n",
"Topic 6: mexico texa mexican oregon california annex minist articl steamer loan\n",
"Topic 7: method relief cent veteran board farmer farm tariff depress committe\n",
"Topic 8: silver gold currenc note circul coinag cent bond coin speci\n",
"Topic 9: soviet communist atom threat aggress ve missil korea weapon ii\n",
"Topic 10: militia british enemi council tribe whilst decre port regular neutral\n",
"Topic 11: gentlemen commission amiti satisfact articl burthen militia prospect majesti hostil\n",
"Topic 12: corpor interst forest island philippin railroad deal class supervis bodi\n",
"Topic 13: kansa slave slaveri june rebellion whilst commenc theconstitut minist south\n",
"Topic 14: vietnam tonight billion budget program tri percent goal crime poverti\n"
]
}
],
"source": [
"model = NMF(n_components=15)\n",
"model.fit(matrix)\n",
"\n",
"n_words = 10\n",
"feature_names = vectorizer.get_feature_names()\n",
"\n",
"topic_list = []\n",
"for topic_idx, topic in enumerate(model.components_):\n",
" top_n = [feature_names[i]\n",
" for i in topic.argsort()\n",
" [-n_words:]][::-1]\n",
" top_features = ' '.join(top_n)\n",
" topic_list.append(f\"topic_{'_'.join(top_n[:3])}\") \n",
"\n",
" print(f\"Topic {topic_idx}: {top_features}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's looking a little more interesting! Lots of references to wars and political conflict, along with slavery and monetary policy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing the outcome\n",
"\n",
"We can get a better handle on what our data looks like through a little visualization. We'll start by loading up the **topic popularity dataframe**. Remember that each row is one of our speeches."
]
},
{
"cell_type": "code",
"execution_count": 365,
"metadata": {},
"outputs": [
{
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" topic_island_cuba_arbitr topic_program_billion_today \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"\n",
" topic_ve_job_tonight topic_spain_coloni_franc topic_terrorist_iraq_iraqi \\\n",
"0 0.0 2.404538 0.0 \n",
"1 0.0 0.000000 0.0 \n",
"\n",
" topic_fight_enemi_japanes topic_mexico_texa_mexican \\\n",
"0 0.116872 0.0 \n",
"1 0.000000 0.0 \n",
"\n",
" topic_method_relief_cent topic_silver_gold_currenc \\\n",
"0 0.0 0.761927 \n",
"1 0.0 0.000000 \n",
"\n",
" topic_soviet_communist_atom topic_militia_british_enemi \\\n",
"0 0.0 0.000000 \n",
"1 0.0 13.178591 \n",
"\n",
" topic_gentlemen_commission_amiti topic_corpor_interst_forest \\\n",
"0 33.359101 0.0 \n",
"1 25.444151 0.0 \n",
"\n",
" topic_kansa_slave_slaveri topic_vietnam_tonight_billion \n",
"0 0.0 0.138286 \n",
"1 0.0 1.364718 "
]
},
"execution_count": 365,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert our counts into numbers\n",
"amounts = model.transform(matrix) * 100\n",
"\n",
"# Set it up as a dataframe\n",
"topics = pd.DataFrame(amounts, columns=topic_list)\n",
"topics.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first row is our first speech, the second row is our second speech, and so on."
]
},
{
"cell_type": "code",
"execution_count": 366,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1b3f15668>"
]
},
"execution_count": 366,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = topics.sum().to_frame().T.plot(kind='barh', stacked=True)\n",
"\n",
"# Move the legend off of the chart\n",
"ax.legend(loc=(1.04,0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, pretty even! A few are larger or smaller, but overall the topics seem pretty evenly distributed.\n",
"\n",
"Looking at things over all time doesn't mean much, though, we're interseted in **change over time**.\n",
"\n",
"The hip way to do this is with a **streamgraph**, which is a stacked area graph that centers on the vertical axis. Usually you'd have to merge the two dataframes in order to graph, but we can sneakily get around it since we aren't plotting with pandas (plotting streamgraphs requires directly talking to matplotlib)."
]
},
{
"cell_type": "code",
"execution_count": 367,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1d088d6d8>"
]
},
"execution_count": 367,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_axis = speeches.year\n",
"y_axis = topics\n",
"\n",
"fig, ax = plt.subplots(figsize=(10,5))\n",
"\n",
"# Plot a stackplot - https://matplotlib.org/3.1.1/gallery/lines_bars_and_markers/stackplot_demo.html\n",
"ax.stackplot(x_axis, y_axis.T, baseline='wiggle', labels=y_axis.columns)\n",
"\n",
"# Move the legend off of the chart\n",
"ax.legend(loc=(1.04,0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I know that \"Presidents talk about current news topics\" is probably not the most exciting things you've ever seen, but you can watch things rise and fall easily enough."
]
},
{
"cell_type": "code",
"execution_count": 368,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1cf975c18>"
]
},
"execution_count": 368,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x216 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"merged = topics.join(speeches)\n",
"\n",
"ax = merged.plot(x='year', y=['topic_kansa_slave_slaveri', 'topic_soviet_communist_atom'], figsize=(10,3))\n",
"ax.legend(loc=(1.04,0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## So what do you do with this?\n",
"\n",
"Good question. TODO.\n",
"\n",
"## Review\n",
"\n",
"In this section we looked at **topic modeling**, a technique of extracting topics out of text datasets. Unlike clustering, where each document is assigned one category, in topic modeling **each document is considered blend of different topics.**\n",
"\n",
"You don't need to \"teach\" a topic model anything about your dataset, you just let it loose and it comes back with what terms represent each topic. The only thing you need to give it is the **number of topics to find**.\n",
"\n",
"The way you preprocess the text is very important to a topic model. We found that common words ended up appearing in many topics unless we used `max_df=` in our vectorizer to filter out high-frequency words.\n",
"\n",
"There are many different algorithms to use for topic modeling, but we're saving that for a later section.\n",
"\n",
"## Discussion topics\n",
"\n",
"TODO"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"toc": {
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