A quick-start guide to extracting named-entities from a Pandas dataframe using spaCy.

A long time ago in a galaxy far away, I was analyzing comments left by customers and I noticed that they seemed to mention specific companies much more than others. This gave me an idea. Maybe there is a way to extract the names of companies from the comments and I could quantify them and conduct further analysis.

There is! Enter: named-entity-recognition.

Named-Entity Recognition

According to Wikipedia, named-entity recognition or NER “is a subtask of information extraction that seeks to locate and classify named entity mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.”¹ In other words, NER attempts to extract words that categorized into proper names and even numerical entities.

In this post, I’ll share the code that will let us extract named-entities from a Pandas dataframe using spaCy, an open-source library provides industrial-strength natural language processing in Python and is designed for production use.²

To get started, let’s install spaCy with the following pip command:

pip install -U spacy

After that, let’s download the pre-trained model for English:

python -m spacy download en

With that out of the way, let’s open up a Jupyter notebook and get started!

Imports

Run the following code block into a cell to get all the necessary imports into our Python environment.

# for manipulating dataframes
import pandas as pd# for natural language processing: named entity recognition
import spacy
from collections import Counter
import en_core_web_sm
nlp = en_core_web_sm.load()# for visualizations
%matplotlib inline

The important line in this block is nlp = en_core_web_sm.load() because this is what we’ll be using later to extract the entities from the text.

Getting the Data

First, let’s get our data and load it into a dataframe. If you want to follow along, download the sample dataset here or create your own from the Trump Twitter Archive.

df = pd.read_csv('ever_trump.csv')

Running df.head() in a cell will get us acquainted with the data set quickly.

Getting the Tokens

Second, let’s create tokens that will serve as input for spaCy. In the line below, we create a variable tokens that contains all the words in the 'text' column of the df dataframe.

tokens = nlp(''.join(str(df.text.tolist())))

Third, we’re going to extract entities. We can just extract the most common entities for now:

items = [x.text for x in tokens.ents]
Counter(items).most_common(20)

Extracting Named-Entities

Next, we’ll extract the entities based on their categories. We have a few to choose from people to events and even organizations. For a complete list of all that spaCy has to offer, check out their documentation on named-entities.

To start, we’ll extract people (real and fictional) using the PERSON type.

person_list = []for ent in tokens.ents:
    if ent.label_ == 'PERSON':
        person_list.append(ent.text)
        
person_counts = Counter(person_list).most_common(20)df_person = pd.DataFrame(person_counts, columns =['text', 'count'])

In the code above, we started by making an empty list with person_list = [].

Then, we utilized a for-loop to loop through the entities found in tokens with tokens.ents. After that, we made a conditional that will append to the previously created list if the entity label is equal to PERSON type.

We’ll want to know how many times a certain entity of PERSON type appears in the tokens so we did with person_counts = Counter(person_list).most_common(20). This line will give us the top 20 most common entities for this type.

Finally, we created the df_person dataframe to store the results and this is what we get:

We’ll repeat the same pattern for the NORP type which recognizes nationalities, religious and political groups.

norp_list = []for ent in tokens.ents:
    if ent.label_ == 'NORP':
        norp_list.append(ent.text)
        
norp_counts = Counter(norp_list).most_common(20)df_norp = pd.DataFrame(norp_counts, columns =['text', 'count'])

And this is what we get:

Bonus Round: Visualization

Let’s create a horizontal bar graph of the df_norp dataframe.

df_norp.plot.barh(x='text', y='count', title="Nationalities, Religious, and Political Groups", figsize=(10,8)).invert_yaxis()

Voilà, that’s it!

I hope you enjoyed this one. Natural language processing is a huge topic but I hope that this gentle introduction will encourage you to explore more and expand your repertoire.

Stay tuned!

You can reach me on Twitter or LinkedIn.

[1]: Wikipedia. (May 22, 2020). Named-entity recognition https://en.wikipedia.org/wiki/Named-entity_recognition

[2]: spaCy. (May 22, 2020). Industrial-Strength Natural Language Processing in Python https://spacy.io/

This article was first published in Towards Data Science‘ Medium publication.

A quick-start guide to creating and visualizing n-gram ranking using nltk for natural language processing.

When I was first starting to learn NLP, I remember getting frustrated or intimidated by information overload so I’ve decided to write a post that covers the bare minimum. You know what they say, “Walk before you run!”

This is a very gentle introduction so we won’t be using any fancy code here.

In a nutshell, natural language processing or NLP simply refers to the process of reading and understanding written or spoken language using a computer. At its simplest use case, we can use a computer to read a book, for example, and count how many times each word was used instead of us manually doing it.

NLP is a big topic and there’s already been a ton of articles written on the subject so we won’t be covering that here. Instead, we’ll focus on how to quickly do one of the simplest but useful techniques in NLP: N-gram ranking.

N-Gram Ranking

Simply put, an n-gram is a sequence of n words where n is a discrete number that can range from 1 to infinity! For example, the word “cheese” is a 1-gram (unigram). The combination of the words “cheese flavored” is a 2-gram (bigram). Similarly, “cheese flavored snack” is a 3-gram (trigram). And “ultimate cheese flavored snack” is a 4-gram (qualgram). So on and so forth.

In n-gram ranking, we simply rank the n-grams according to how many times they appear in a body of text — be it a book, a collection of tweets, or reviews left by customers of your company.

Let’s get started!

Getting the Data

First, let’s get our data and load it into a dataframe. You can download the sample dataset here or create your own from the Trump Twitter Archive.

import pandas as pddf = pd.read_csv('tweets.csv')

Using df.head() we can quickly get acquainted with the dataset.

Importing Packages

Next, we’ll import packages so we can properly set up our Jupyter notebook:

# natural language processing: n-gram ranking
import re
import unicodedata
import nltk
from nltk.corpus import stopwords# add appropriate words that will be ignored in the analysis
ADDITIONAL_STOPWORDS = ['covfefe']
import matplotlib.pyplot as plt

In the code block above, we imported pandas so that we can shape and manipulate our data in all sorts of different and wonderful ways! Next, we imported re for regex, unicodedata for Unicode data, and nltk to help with parsing the text and cleaning them up a bit. And then, we specified additional stop words that we want to ignore. This is helpful in trimming down the noise. Lastly, we imported matplotlib matplotlib so we can visualize the result of our n-gram ranking later.

Next, let’s create a function that will perform basic cleaning of the data.

Basic Cleaning

def basic_clean(text):
  """
  A simple function to clean up the data. All the words that
  are not designated as a stop word is then lemmatized after
  encoding and basic regex parsing are performed.
  """
  wnl = nltk.stem.WordNetLemmatizer()
  stopwords = nltk.corpus.stopwords.words('english') + ADDITIONAL_STOPWORDS
  text = (unicodedata.normalize('NFKD', text)
    .encode('ascii', 'ignore')
    .decode('utf-8', 'ignore')
    .lower())
  words = re.sub(r'[^\w\s]', '', text).split()
  return [wnl.lemmatize(word) for word in words if word not in stopwords]

The function above takes in a list of words or text as input and returns a cleaner set of words. The function does normalization, encoding/decoding, lower casing, and lemmatization.

Let’s use it!

words = basic_clean(''.join(str(df['text'].tolist())))

Above, we’re simply calling the function basic_lean() to process the 'text' column of our dataframe df and making it a simple list with tolist(). We then assign the results to words.

N-grams

Here comes the fun part! In one line of code, we can find out which bigrams occur the most in this particular sample of tweets.

(pd.Series(nltk.ngrams(words, 2)).value_counts())[:10]

We can easily replace the number 2 with 3 so we can get the top 10 trigrams instead.

(pd.Series(nltk.ngrams(words, 3)).value_counts())[:10]

Voilà! We got ourselves a great start. But why stop now? Let’s try it and make a little eye candy.

Bonus Round: Visualization

To make things a little easier for ourselves, let’s assign the result of n-grams to variables with meaningful names:

bigrams_series = (pd.Series(nltk.ngrams(words, 2)).value_counts())[:12]trigrams_series = (pd.Series(nltk.ngrams(words, 3)).value_counts())[:12]

I’ve replaced [:10] with [:12] because I wanted more n-grams in the results. This is an arbitrary value so you can choose whatever makes the most sense to you according to your situation.

Let’s create a horizontal bar graph:

bigrams_series.sort_values().plot.barh(color='blue', width=.9, figsize=(12, 8))

And let’s spiffy it up a bit by adding titles and axis labels:

bigrams_series.sort_values().plot.barh(color='blue', width=.9, figsize=(12, 8))
plt.title('20 Most Frequently Occuring Bigrams')
plt.ylabel('Bigram')
plt.xlabel('# of Occurances')

And that’s it! With a few simple lines of code, we quickly made a ranking of n-grams from a Pandas dataframe and even made a horizontal bar graph out of it.

I hope you enjoyed this one. Natural Language Processing is a big topic but I hope that this gentle introduction will encourage you to explore more and expand your repertoire.

In the next article, we’ll visualize an n-gram ranking in Power BI with a few simple clicks of the mouse and a dash of Python!

Stay tuned!

You can reach me on Twitter or LinkedIn.

This article was first published on Towards Data Science‘ Medium publication.