Gain insights into text preprocessing with Python. Explore text cleaning, normalization, and advanced techniques like BoW and TF-IDF. Discover skills to handle unstructured data effectively for NLP.

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Text Processing

This course is designed to empower you with essential skills for effectively handling text data in the context of natural language processing (NLP). You’ll embark on a transformative journey that will equip you with a solid foundation in text manipulation, enabling you to tackle the challenges of unstructured data. 

The course discusses both fundamental and advanced text preprocessing techniques. You’ll learn how to clean text and remove noise, irrelevant characters, and inconsistencies in text data. Once the data is ready for analysis, you’ll learn text normalization techniques such as stemming, lemmatization, and casing. In addition to mastering preprocessing fundamentals, you’ll also learn techniques such as bag-of-words (BoW) and term frequency-inverse document frequency (TF-IDF).

By the end of the course, you’ll be able to position yourself for success in a data-centric world where the ability to extract meaning from unstructured textual information is a prized skill.

Text Preprocessing with Python

timestamp,username,feedback,rating
2023-08-08 10:00:00,@TechEnthusiast,"The new telecom product offers amazing connectivity and lightning-fast speeds. I'm thoroughly impressed!",5
2023-08-08 10:15:00,@GadgetGuru,"The new telecom product is a game-changer! It's made my online gaming experience so much smoother and lag-free.",4
2023-08-08 10:30:00,@FrequentCaller,"I've noticed a significant improvement in call quality and signal strength with the new telecom product. Great job!",4.5
2023-08-08 10:45:00,@BusinessOwner,"The new product has enhanced our business operations by providing reliable internet for all our devices. A must-have for any office.",5
2023-08-08 11:00:00,@DigitalNomad,"As a digital nomad, I rely on consistent internet wherever I go. The new telecom product has kept me connected no matter where I am!",4.5
2023-08-08 11:15:00,@ConcernedUser,"While the new product offers good speeds, I experienced occasional dropouts in my connection. Hoping for a fix soon.",3
2023-08-08 11:30:00,@SocialMediaAddict,"Streaming videos and uploading content has never been smoother. The new telecom product has improved my online presence!",5
2023-08-08 11:45:00,@BudgetShopper,"The new telecom product is fantastic, but the pricing seems a bit steep. I'd love to see more affordable options.",4
2023-08-08 12:00:00,@TechNovice,"I was hesitant at first, but the setup process was surprisingly easy. The new telecom product is user-friendly even for beginners like me.",4.5
2023-08-08 12:15:00,@PowerUser,"I heavily rely on fast internet for my work, and the new product has exceeded my expectations. It's a definite upgrade.",5

import pandas as pd
 
feedback_df = pd.read_csv('feedback.csv')  
feedback_df['feedback'] = feedback_df['feedback'].str.lower()  
print(feedback_df['feedback'])

import pandas as pd

feedback_df = pd.read_csv("feedback.csv")

import pandas as pd

# Load the feedback dataset
feedback_df = pd.read_csv('feedback.csv') 

# Convert the feedback column into lowercase 
feedback_df['feedback'] = feedback_df['feedback'].str.lower() 

# Print the updated feedback column
print(feedback_df['feedback'])

Review solution explanations for the code challenges on basic preprocessing techniques.

About This Course

Introduction To Text Preprocessing

Regular Expressions

Irrelevant Text Data

Basic Text Preprocessing Techniques

Indexing

Text Transformation

Text Representation

Text Feature Engineering

Advanced Text Preprocessing

N-grams

Text Classification of Customer Reviews

Conclusion

Text Classification Using PyTorch

Solution Explanations: Basic Preprocessing Techniques

Solution 1: Lowercasing text