A significant portion of the data that’s generated today is unstructured. Unstructured data includes social media comments, browsing history, and customer feedback. Have you found yourself in a situation with a bunch of textual data to analyze, and no idea how to proceed? Natural language processing in Python can help.
The objective of this tutorial is to enable you to analyze textual data in Python through the concepts of natural language processing (NLP). You’ll first learn how to tokenize your text into smaller chunks, normalize words to their root forms, and then remove any noise in your documents to prepare them for further analysis.
Let’s get started!
In this tutorial, we’ll use Python’s
nltk library to perform all NLP operations on the text. At the time of writing this tutorial, we’re using version 3.4 of
nltk. To install the library, you can use the
pip command on the terminal:
pip install nltk==3.4
To check which version of
nltk you have in the system, you can import the library into the Python interpreter and check the version:
import nltk print(nltk.__version__)
To perform certain actions within
nltk in this tutorial, you may have to download specific resources. We’ll describe each resource as and when required.
However, if you’d like to avoid downloading individual resources later in the tutorial and grab them now in one go, run the following command:
python -m nltk.downloader all
Step 1: Convert into Tokens
A computer system cann’t find meaning in natural language by itself. The first step in processing natural language is to convert the original text into tokens. A token is a combination of continuous characters, with some meaning. It’s up to you to decide how to break a sentence into tokens. For instance, an easy method is to split a sentence by whitespace to break it into individual words.
In the NLTK library, you can use the
word_tokenize() function to convert a string to tokens. However, you’ll first need to download the
punkt resource. Run the following command in the terminal:
Next, you need to import
nltk.tokenize to use it:
from nltk.tokenize import word_tokenize print(word_tokenize("Hi, this is a nice hotel."))
The output of the code is as follows:
['Hi', ',', 'this', 'is', 'a', 'nice', 'hotel', '.']
You’ll notice that
word_tokenize doesn’t simply split a string based on whitespace, but also separates punctuation into tokens. It’s up to you if you’d like to retain the punctuation marks in the analysis.
Step 2: Convert Words to their Base Forms
When you’re processing natural language, you’ll often notice that there are various grammatical forms of the same word. For instance, “go”, “going” and “gone” are forms of the same verb, “go”.
While the necessities of your project may require you to retain words in various grammatical forms, let’s discuss a way to convert various grammatical forms of the same word into its base form. There are two techniques that you can use to convert a word to its base.
The first technique is stemming. Stemming is a simple algorithm that removes affixes from a word. There are various stemming algorithms available for use in NLTK. We’ll use the Porter algorithm in this tutorial.
We first import
nltk.stem.porter. Next, we initialize the stemmer to the
stemmer variable, and then use the
.stem() method to find the base form of a word:
from nltk.stem.porter import PorterStemmer stemmer = PorterStemmer() print(stemmer.stem("going"))
The output of the code above is
go. If you run the stemmer for the other forms of “go” described above, you’ll notice that the stemmer returns the same base form, “go”. However, as stemming is only a simple algorithm based on removing word affixes, it fails when the words are less commonly used in language.
For example, when you try the stemmer on the word “constitutes”, it gives an unintuitive result:
You’ll notice the output is “constitut”.
This issue is solved by moving on to a more complex approach to finding the base form of a word in a given context. The process is called lemmatization. Lemmatization normalizes a word based on the context and vocabulary of the text. In NLTK, you can lemmatize sentences using the
First, you need to download the
wordnet resource from the NLTK downloader in the Python terminal:
Once it’s downloaded, you need to import the
WordNetLemmatizer class and initialize it:
from nltk.stem.wordnet import WordNetLemmatizer lem = WordNetLemmatizer()
To use the lemmatizer, use the
.lemmatize() method. It takes two arguments: the word, and the context. In our example, we’ll use “v” for context. Let’s explore the context further after looking at the output of the
You’ll notice that the
.lemmatize() method correctly converts the word “constitutes” to its base form, “constitute”. You’ll also notice that lemmatization takes longer than stemming, as the algorithm is more complex.
Let’s check how to determine the second argument of the
.lemmatize() method programmatically. NLTK has a
pos_tag() function that helps in determining the context of a word in a sentence. However, you first need to download the
averaged_perceptron_tagger resource through the NLTK downloader:
Next, import the
pos_tag() function and run it on a sentence:
from nltk.tag import pos_tag sample = "Hi, this is a nice hotel." print(pos_tag(word_tokenize(sample)))
You’ll notice that the output is a list of pairs. Each pair consists of a token and its tag, which signifies the context of a token in the overall text. Notice that the tag for a punctuation mark is itself:
[('Hi', 'NNP'), (',', ','), ('this', 'DT'), ('is', 'VBZ'), ('a', 'DT'), ('nice', 'JJ'), ('hotel', 'NN'), ('.', '.')]
How do you decode the context of each token? Here’s a full list of all tags and their corresponding meanings on the Web. Notice that the tags of all nouns begin with “N”, and for all verbs begin with “V”. We can use this information in the second argument of our
def lemmatize_tokens(stentence): lemmatizer = WordNetLemmatizer() lemmatized_tokens =  for word, tag in pos_tag(stentence): if tag.startswith('NN'): pos = 'n' elif tag.startswith('VB'): pos = 'v' else: pos = 'a' lemmatized_tokens.append(lemmatizer.lemmatize(word, pos)) return lemmatized_tokens sample = "Legal authority constitutes all magistrates." print(lemmatize_tokens(word_tokenize(sample)))
The output of the code above is as follows:
['Legal', 'authority', 'constitute', 'all', 'magistrate', '.']
This output is expected, where “constitutes” and “magistrates” have been converted to “constitute” and “magistrate” respectively.
Step 3: Data Cleaning
The next step in preparing data is to clean the data and remove anything that doesn’t add meaning to your analysis. Broadly, we’ll look at removing punctuation and stop words from your analysis.
Removing punctuation is a fairly easy task. The
punctuation object of the
string library contains all the punctuation marks in English:
import string print(string.punctuation)
The output of this code snippet is as follows:
In order to remove punctuation from tokens, you can simply run this:
for token in tokens: if token in string.punctuation: # Do something
Next, we’ll focus on removing stop words. Stop words are commonly-used words in language like “I”, “a” and “the”, which add little meaning to text when analyzing it. So we’ll remove stop words from our analysis. First, download the
stopwords resource from the NLTK downloader:
Once your download is complete, import
nltk.corpus and use the
.words() method with “english” as the argument. It’s a list of 179 stop words in the English language:
from nltk.corpus import stopwords stop_words = stopwords.words('english')
We can combine the lemmatization example with the concepts discussed in this section to create the following function,
clean_data(). Additionally, before comparing if a word is a part of the stop words list, we convert it to lowercase. This way, we still capture a stop word if it occurs at the start of a sentence and is capitalized:
def clean_data(tokens, stop_words = ()): cleaned_tokens =  for token, tag in pos_tag(tokens): if tag.startswith("NN"): pos = 'n' elif tag.startswith('VB'): pos = 'v' else: pos = 'a' lemmatizer = WordNetLemmatizer() token = lemmatizer.lemmatize(token, pos) if token not in string.punctuation and token.lower() not in stop_words: cleaned_tokens.append(token) return cleaned_tokens sample = "The quick brown fox jumps over the lazy dog." stop_words = stopwords.words('english') clean_data(word_tokenize(sample), stop_words)
The output of the example is as follows:
['quick', 'brown', 'fox', 'jump', 'lazy', 'dog']
As you can see, the punctuation and stop words have been removed.
Word Frequency Distribution
Now that you’re familiar with the basic cleaning techniques in NLP, let’s try to find the frequency of words in text. For this exercise, we’ll use the text of the fairy tale, The Mouse, The Bird and The Sausage, which is available freely on Gutenberg. We’ll store the text of this fairy tale in a string,
First, we tokenize
text and then clean it using the function
clean_data that we defined above:
tokens = word_tokenize(text) cleaned_tokens = clean_data(tokens, stop_words = stop_words)
To find the frequency distribution of words in your text, you can use the
FreqDist class of NLTK. Initialize the class with the tokens as an argument. Then use the
.most_common() method to find the commonly occurring terms. Let’s try to find the top ten terms in this case:
from nltk import FreqDist freq_dist = FreqDist(cleaned_tokens) freq_dist.most_common(10)
Here are the ten most commonly occurring terms in this fairy tale:
[('bird', 15), ('sausage', 11), ('mouse', 8), ('wood', 7), ('time', 6), ('long', 5), ('make', 5), ('fly', 4), ('fetch', 4), ('water', 4)]
Unsurprisingly, the three most common terms are the three main characters in the fairy tale.
The frequency of words may not be very important when analyzing text. Typically, the next step in NLP is to generate a statistic — TF-IDF (term frequency — inverse document frequency) — which signifies the importance of a word in a list of documents.
In this tutorial, we’ve taken a first look at natural language processing in Python. We converted text to tokens, converted words to their base forms and, finally, cleaned the text to remove any part that didn’t add meaning to the analysis.
Although we’ve looked at simple NLP tasks in this tutorial, there are many more techniques to explore. We might, for example, want to perform topic modelling on textual data, where the objective is to find a common topic that a text might be talking about. A more complex task in NLP is the implementation of a sentiment analysis model to determine the feeling behind any text.
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