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From Words to Wisdom
- Text data is unstructured and requires specialized techniques in data science to extract useful information.
- Text preprocessing is a critical step in making raw text data ready for analysis.
- Stemming algorithms and lemmatization are two methods of text preprocessing, with lemmatization being more precise.
- The Bag of Words (BoW) model and Term Frequency-Inverse Document Frequency (TF-IDF) are foundational techniques used in text analysis and natural language processing.
- BoW treats text as a mere collection of words, ignoring the grammar and the order in which words appear.
- TF-IDF is a statistical measure used to evaluate how important a word is to a document in a collection or corpus.
- BoW and TF-IDF have limitations in handling synonyms and polysemy.
- In handling large amounts of text data, BoW can result in high-dimensional and sparse data.
- Without additional processing like stop-word removal or term weighting, BoW models may be biased towards frequent, less informative words.
- Service industry companies like hotels or airlines collect vast amounts of text data to enhance customer satisfaction, improve services, and tailor marketing strategies.
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