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,sentiment
2023-08-08 10:00:00,@TechEnthusiast,"The new telecom product offers amazing connectivity and lightning-fast speeds. I'm thoroughly impressed!",positive
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.",positive
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!",positive
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.",positive
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!",positive
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.",neutral
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!",positive
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.",positive
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.",positive
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.",positive

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize import word_tokenize
import string

feedback_df = pd.read_csv('feedback.csv')
def preprocess(text):
    text = text.lower()
    translator = str.maketrans('', '', string.punctuation)
    text = text.translate(translator)
    return text
feedback_df['feedback'] = feedback_df['feedback'].apply(preprocess)
vectorizer = CountVectorizer(tokenizer=word_tokenize, ngram_range=(2, 3))
X = vectorizer.fit_transform(feedback_df['feedback'])
grams = vectorizer.get_feature_names()
print(grams)

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize import word_tokenize
import string

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

grams = [None] * 20

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize import word_tokenize
import string

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

# Define a function to lowercase and remove punctuation
def preprocess(text):
    text = text.lower()
    translator = str.maketrans('', '', string.punctuation)
    text = text.translate(translator)
    return text

# Apply the 'preprocess' function to the 'feedback' column
feedback_df['feedback'] = feedback_df['feedback'].apply(preprocess)

# Initialize a CountVectorizer with specified parameters
vectorizer = CountVectorizer(tokenizer=word_tokenize, ngram_range=(2, 3))

# Transform the 'feedback' text data into a matrix of token counts
X = vectorizer.fit_transform(feedback_df['feedback'])

# Get the list of generated bigrams from the vectorizer
grams = vectorizer.get_feature_names()

Review solution explanations for the code challenges on n-grams.

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: N-Grams

Solution 1: Introduction to n-grams