Overview of Text Preprocessing and Its Importance

Get introduced to text preprocessing in Python, its examples, and its techniques.

Introduction

Text preprocessing refers to tasks and techniques we perform on raw text data before further analysis. These techniques are critical for organizations looking to uncover insights from text data, i.e., customer review data, social media posts, news headlines, etc. Such organizations could be in various domains, including business, academia, healthcare, social media, customer service, and data science.

A few examples of text preprocessing techniques include:

  • Lowercasing: This technique entails converting all text to lowercase, which helps avoid duplication and inconsistency in subsequent processing steps.

  • Removing duplicate words: This technique eliminates duplicate occurrences of words in the text, which helps to avoid overemphasis on repeated words and ensures a more balanced representation of the text data.

  • Removing special characters: This technique removes special characters, such as hashtags, mentions, or non-alphanumeric characters, that might not contribute much to the analysis and can be safely removed.

  • Stopword removal: We perform this technique to remove stopwords, which are common words that don’t carry much meaning or contribute to understanding the text. Removing such words helps to reduce noise and computational overhead in downstream tasks like text classification, sentiment analysis, or topic modeling.

  • Tokenization: We perform tokenization to break the text into individual words or tokens. This is a fundamental step in NLP tasks, allowing us to analyze and process text word by word.

  • Stemming: This technique involves reducing a word to its root form, known as a stem, by removing suffixes and prefixes. The objective is to simplify text analysis by reducing words to their basic form. For example, the word “running” might be stemmed to “run,” but this could also change the meaning of the sentence.

  • Lemmatization: This is a more advanced technique that involves reducing words to their base form, known as a lemma, using a dictionary-based approach. When using this technique, we consider the context and part of speech of the word to ensure that the resulting lemma is a valid word in the language. The goal is to reduce word variations and therefore, improve the accuracy of NLP tasks such as text classification, sentiment analysis, and information retrieval. For example, we can lemmatize the word “singing” to “sing” (verb base form) and “songs” to “song” (noun singular).

We’ll explore additional text preprocessing techniques, including handling irrelevant text data, transforming text, part-of-speech tagging, named entity recognition, chunking, text feature engineering, working with n-grams, and text representation.

Importance of text preprocessing

Text preprocessing is crucial for data science and machine learning. In data science, we use text preprocessing techniques for data cleaning and preprocessing, which involves removing irrelevant information from text data and transforming it into a more structured format that can be used for analysis. On the other hand, in machine learning, we use text preprocessing techniques to create datasets for training machine learning models. For example, a sentiment analysis model might be trained on a large corpus of text data to recognize positive or negative sentiment. In contrast, a text classification model might be trained to categorize text data into different topics or genres.

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Text preprocessing in data analysis and machine learning
Text preprocessing in data analysis and machine learning

Applications

Text preprocessing has a wide range of real-world applications across various industries:

  • In finance, we use text preprocessing techniques to analyze news articles and social media posts, predict stock prices, identify emerging trends, and monitor market sentiment.
  • In healthcare, we use text processing techniques when analyzing electronic medical records and clinical notes and identifying patterns and trends in patient data.
  • In marketing, we use text preprocessing techniques to analyze customer reviews, social media posts, and other customer feedback forms to identify customer needs, preferences, and sentiments.
  • We apply text preprocessing techniques to analyze contracts, legal documents, and regulatory filings to identify key clauses and obligations in legal and regulatory compliance.

Text preprocessing tools

We can use many tools to apply text preprocessing techniques. Some of the most commonly used tools include:

  • Python libraries: These include regular expressions, Natural Language Toolkit (NLTK), spaCy, scikit-learn, TextBlob, and Gensim.

  • Apache OpenNLP: This is an open-source library for natural language processing that provides various text preprocessing functionalities. The Apache Software Foundation developed the library, which is written in Java.

  • Stanford CoreNLP: This is a suite of natural language processing tools developed by Stanford University.

  • IBM Watson Natural Language Understanding: This is a cloud-based text processing tool that provides various NLP functionalities to analyze unstructured text data. It uses machine learning and deep learning techniques to extract insights and metadata from text data.

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Text preprocessing tools
Text preprocessing tools

Text preprocessing technique: Code example

Let’s explore a code example showcasing a text preprocessing technique by running the code below. We’ll use Python to demonstrate removing special characters from the reviews.csv file in the code. By removing such characters, we standardize the text, making detecting patterns easier, performing sentiment analysis, or extracting meaningful features for training machine-learning models.

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reviews.csv
import pandas as pd
import re
df = pd.read_csv('reviews.csv')
def remove_special_characters(text):
clean_text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
return clean_text
df['clean_text'] = df['review_text'].apply(remove_special_characters)
print(df['clean_text'])

Let’s review the code line by line:

  • Lines 1–2: We import the pandas library for data manipulation and the re module for regular expressions.

  • Line 4: We load the reviews.csv dataset into the pandas DataFrame called df.

  • Lines 5–7: We then define a remove_special_characters function to remove special characters from the review_text column. We then return the cleaned text.

  • Line 8: We apply the function to the review_text column of the df DataFrame using the apply() method and create a new column called clean_text in the DataFrame to store the cleaned text.

  • Line 9: We display the clean_text column to see the preprocessed data.

With just a few lines of code, we’ve gotten ourselves ready for further analysis.