Topic: Handling Datasets of All Types – Part 4 of 5: Text Data Processing and Natural Language Processing (NLP)

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1. Understanding Text Data

• Text data is unstructured and requires preprocessing to convert into numeric form for ML models.

• Common tasks: classification, sentiment analysis, language modeling.

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2. Text Preprocessing Steps

• Tokenization: Splitting text into words or subwords.

• Lowercasing: Convert all text to lowercase for uniformity.

• Removing Punctuation and Stopwords: Clean unnecessary words.

• Stemming and Lemmatization: Reduce words to their root form.

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3. Encoding Text Data

• Bag-of-Words (BoW): Represents text as word count vectors.

• TF-IDF (Term Frequency-Inverse Document Frequency): Weighs words based on importance.

• Word Embeddings: Dense vector representations capturing semantic meaning (e.g., Word2Vec, GloVe).

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4. Loading and Processing Text Data in Python

from sklearn.feature_extraction.text import TfidfVectorizer

texts = ["I love data science.", "Data science is fun."]
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(texts)

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5. Handling Large Text Datasets

• Use libraries like NLTK, spaCy, and Transformers.

• For deep learning, tokenize using models like BERT or GPT.

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6. Summary

• Text data needs extensive preprocessing and encoding.

• Choosing the right representation is crucial for model success.

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Exercise

• Clean a set of sentences by tokenizing and removing stopwords.

• Convert cleaned text into TF-IDF vectors.

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#NLP #TextProcessing #DataScience #MachineLearning #Python

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