As exemplified by the popularity of blogging and social media, textual data is far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products.
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What You'll Learn
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Tokenization, stemming, and n-grams
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The bag-of-words and vector space models
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Feature engineering for textual data (e.g. cosine similarity between documents)
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Feature extraction using singular value decomposition (SVD)
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Training classification models using textual data
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Evaluating accuracy of the trained classification models
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Requirements
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