Transforming Text Data with Effective Feature Engineering in NLP

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Feature engineering in Natural Language Processing (NLP) involves transforming raw text data into a format that can be effectively utilized by machine learning algorithms. It plays a crucial role in extracting meaningful patterns and information from textual data. Here are key aspects of feature engineering in NLP:

1. Text Preprocessing:

• Tokenization: Splitting text into individual words or tokens.
• Lowercasing: Converting all text to lowercase to ensure uniformity.
• Removing Punctuation and Special Characters: Cleaning text by eliminating unnecessary symbols.
• Stopword Removal: Removing common words (stopwords) that often do not contribute significant meaning.
• Stemming and Lemmatization: Reducing words to their root form to handle variations (e.g., “running” to “run”).

2. Bag-of-Words (BoW) Representation:

• Count Vectorization: Creating a matrix representing the count of each word in a document.
• Term Frequency-Inverse Document Frequency (TF-IDF): Assigning weights to words based on their frequency in a document relative to the entire corpus.

3. Word Embeddings: