NLP Preprocessing in Python

Natural language processing (NLP) is a field of computer science that focuses on the interaction between computers and humans through the use of natural language. It has a wide range of applications, including language translation, sentiment analysis, and text summarization.

One of the first steps in many NLP tasks is preprocessing the text data. Preprocessing involves a series of steps that are performed on the raw text to clean and prepare it for further analysis. These steps can include lowercasing, stemming, lemmatization, and removing stop words and punctuation.

In this blog post, we will look at the different steps involved in preprocessing text data in Python.


The first step in preprocessing is often lowercasing the text. This involves converting all the words to lowercase so that the model does not treat words with the same spelling but different capitalization as different entities.

To lowercase the text, we can use the lower() method in Python. For example:

text = "This is some text."
lowercase_text = text.lower()
print(lowercase_text) # this is some text.

Stemming and lemmatization

Another common step in preprocessing is stemming and lemmatization. Stemming involves reducing words to their base form, or stem, by removing suffixes and prefixes. This is useful for tasks such as information retrieval, where we want to group together words with the same root meaning even if they have different inflections.

Lemmatization, on the other hand, involves reducing words to their base form by considering the context and part of speech. This is useful for tasks such as text classification, where we want to treat words with the same meaning as the same entity.

To stem and lemmatize words in Python, we can use the nltk library. First, we will need to install it by running pip install nltk. Then, we can use the PorterStemmer and WordNetLemmatizer classes from nltk.stem to stem and lemmatize words, respectively.

For example:

import nltk
from nltk.stem import PorterStemmer, WordNetLemmatizer

text = "running run runs runner"

# Stem the words using Porter stemmer
porter_stemmer = PorterStemmer()
stemmed_words = [porter_stemmer.stem(word) for word in text.split()]
print(stemmed_words) # ['run', 'run', 'run', 'runner']

# Lemmatize the words using WordNet lemmatizer
wordnet_lemmatizer = WordNetLemmatizer()
lemmatized_words = [wordnet_lemmatizer.lemmatize(word) for word in text.split()]
print(lemmatized_words) # ['running', 'run', 'runs', 'runner']

Removing stop words and punctuation

Stop words are words that are commonly used in a language but do not carry much meaning, such as “a,” “an,” “the,” etc. They are often removed from text data as they do not contribute much to the overall meaning of the text.

Punctuation marks are also usually removed from text data as they do not carry any meaning and can interfere with the analysis.

To remove stop words and punct

uation marks in Python, we can use the nltk library. First, we will need to download the list of stop words by running'stopwords'). Then, we can use the stopwords and string modules to remove stop words and punctuation marks from the text.

For example:

import nltk
import string
from nltk.corpus import stopwords

text = "This is some text with stop words and punctuation marks."

# Remove stop words
stop_words = set(stopwords.words('english'))
filtered_text = " ".join([word for word in text.split() if word not in stop_words])
print(filtered_text) # "text stop words punctuation marks."

# Remove punctuation marks
filtered_text = "".join([word for word in filtered_text if word not in string.punctuation])
print(filtered_text) # text stop words punctuation marks

Final thoughts

Preprocessing is an important step in NLP as it helps to clean and prepare the text data for further analysis. In this blog post, we have looked at some of the common preprocessing steps, including lowercasing, stemming and lemmatization, and removing stop words and punctuation marks. We have also seen how to implement these steps in Python using the nltk library.