Text Preprocessing - Stop Words 🚫📝

#04 - 100 Days of NLP

Hello again, NLP enthusiasts 🤟 On Day 4 of our 100 Days of NLP journey, we’re diving into the world of stop words. These common words might not seem significant, but knowing how to handle them is crucial for effective text preprocessing. Let’s get started! 🚀

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What are Stop Words?

Stop words are commonly used words in a language that are usually filtered out before processing text. Examples include words like "and", "the", "is", and "in". These words are essential for human communication but often don't add much value for text analysis.

Why Remove Stop Words?

Removing stop words helps in:

1. Reducing Noise: Eliminates common words that may not contribute to the meaning of the text.

2. Improving Efficiency: Decreases the amount of data to process, speeding up analysis.

3. Enhancing Model Performance: Focuses on the more meaningful words, which can improve the accuracy of models.

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Practical Examples

Let’s see how we can remove stop words using Python. Remember, don't worry about the implementation details now—we'll tackle those with libraries like SpaCy or NLTK later. 😅

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

text = "This is a sample sentence, showing off the stop words filtration."

stop_words = set(stopwords.words('english'))
word_tokens = word_tokenize(text)

filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words]

print("Original Sentence:", word_tokens)
print("Filtered Sentence:", filtered_sentence)

# OUTPUT

Original Sentence: ['This', 'is', 'a', 'sample', 'sentence', ',', 'showing', 'off', 'the', 'stop', 'words', 'filtration', '.']

Filtered Sentence: ['sample', 'sentence', ',', 'showing', 'stop', 'words', 'filtration', '.']

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Fun NLP Application: Information Retrieval 📂🔍

Removing stop words is essential in applications like information retrieval. For example, when you search for "best places to eat in New York" on Google, the search engine ignores stop words like "to" and "in", focusing on "best places eat New York". This helps deliver more relevant results. 🍕🗽

What's Next?

Now that you understand stop words and their importance, tomorrow we'll dive into stemming and lemmatization. These techniques will help us further refine our text preprocessing skills. Stay tuned! 📚✨

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Excited to continue this journey with you! Let’s make some NLP magic together! 🎉