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Tokenize pandas column5/2/2023 But frequent words carrying little meaning, the so-called stop words, introduce noise into machine learning and data analysis because they make it harder to detect patterns.įigure 4-1. The raw data may include HTML tags or special characters that should be removed in most cases. When working with text, noise comes in different flavors. Whatâs noise and what isnât always depends on the analysis you are going to perform. Correctly identifying such word sequences as compound structures requires sophisticated linguistic processing.ĭata preparation or data preprocessing in general involves not only the transformation of data into a form that can serve as the basis for analysis but also the removal of disturbing noise. Think of the word sequence New York, which should be treated as a single named-entity. To build models on the content, we need to transform a text into a sequence of words or, more generally, meaningful sequences of characters called tokens. Technically, any text document is just a sequence of characters. Preparing Textual Data for Statistics and Machine Learning
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