Description

In this tutorial, you’ll dive into the essential techniques for processing unstructured text data — a common challenge in fields like natural language processing (NLP), sentiment analysis, and text mining. Text data often needs to be cleaned, tokenized, and transformed into a structured form before it can be analyzed or used in machine learning models.

You’ll start by learning how to load and read text data from various sources such as CSV files, JSON, or text files. Once the text is loaded, you’ll explore basic text preprocessing tasks, including removing punctuation, converting to lowercase, and eliminating unnecessary whitespace.

A major part of text processing is tokenization — breaking text into smaller units, such as words or sentences. You’ll practice using tokenizers to split text into words, and explore advanced techniques like lemmatization (reducing words to their base form, e.g., “running” → “run”) and stemming (removing suffixes, e.g., “running” → “run”).

You’ll also learn how to remove stop words (common words like “the”, “and”, “is” that don’t add much value to analysis) and handle special characters such as URLs, email addresses, and numbers. These techniques will help clean the data and make it more meaningful for analysis.

Once the data is preprocessed, you’ll explore how to convert text to numerical representations using techniques like Bag-of-Words (counting word frequencies) and TF-IDF (Term Frequency-Inverse Document Frequency), which is useful for understanding the importance of words in a document relative to the entire dataset.

In the hands-on section, you’ll apply these techniques to a real-world text dataset (e.g., product reviews, tweets, or news articles). You’ll preprocess the text, perform tokenization and lemmatization, remove stop words, and convert the text into a numerical format suitable for analysis or machine learning.

By the end of this tutorial, you’ll have a solid foundation in text processing and be ready to tackle more advanced tasks like feature extraction for text classification, sentiment analysis, or topic modeling.

You’ll also receive a Jupyter Notebook with practical examples, a PDF summary of key text preprocessing functions, and exercises to test and reinforce your skills.