A Deep Dive into Text Classification with TF-IDF

Introduction:

Unlocking the potential within textual data is a rewarding journey, and text classification, a cornerstone of Natural Language Processing (NLP), stands as a beacon in this exploration. In this blog post, we delve into the intricacies of text classification using Python, Pandas, NLTK, and scikit-learn. Our practical example revolves around travel and food-related sentences, illustrating the application of TF-IDF (Term Frequency-Inverse Document Frequency) in extracting meaningful insights.

Setting up the Data:

Our dataset encapsulates the essence of travel and food experiences, with each sentence tagged with a category (‘t’ for travel and ‘f’ for food).

import pandas as pd

content = ["i will be travelling to mumbai in train", 
           "i will be eating in train", 
           "i love travel alot", 
           "i love to eat south indian food"]

classes = ['t','f','t','f']

dic = {'category': classes, 'description': content}

df = pd.DataFrame(dic)

The table representation of the data is as follows:

category	description
t	i will be travelling to mumbai in train
f	i will be eating in train
t	i love travel a lot
f	i love to eat south indian food

Text Preprocessing:

A crucial step before classification involves text preprocessing, including stemming to reduce words to their root form. Here, the PorterStemmer from NLTK aids in this transformation.

from nltk.stem import PorterStemmer

ps = PorterStemmer()

all_words = " ".join(content)
stem_words = [ps.stem(w) for w in all_words.split()]
vocabulary = set(stem_words)

Feature Extraction with TF-IDF:

Moving forward, the TF-IDF Vectorizer from scikit-learn transforms our raw text data into numerical features, assigning weights to words based on their importance in each document and across the entire corpus.

from sklearn.feature_extraction.text import TfidfVectorizer

corpus = [
    'This is the first document.',
    'This document is the second document.',
    'And this is the third one.',
    'Is this the first document?'
]

vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus).todense()

words = vectorizer.get_feature_names()
sentences = [sentence for sentence in corpus]

df_transformed = pd.DataFrame(X, index=sentences, columns=words)

Unveiling Insights:

Our journey through text classification reveals the significance of text preprocessing and TF-IDF in deciphering meaningful patterns within textual data. The amalgamation of NLP techniques and machine learning tools empowers data enthusiasts to navigate and derive insights from diverse text datasets.

Conclusion:

In conclusion, this exploration showcases the transformative potential of NLP and TF-IDF in the realm of text analysis. Armed with the knowledge of text preprocessing, feature extraction, and classification techniques, analysts and data scientists can unravel valuable insights from the ever-expanding realm of textual information, enhancing decision-making processes across various domain.

About the Author:

I am Kishore Kumar K, a dedicated data scientist with a passion for unraveling insights hidden within complex datasets. With a background in MBA in Business Analytics and a BCA in Computer Applications, I have honed my skills in statistical analysis, machine learning, and data visualization.

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