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Text preprocessing is a crucial first step in transforming unstructured text into machine-readable data. It involves cleaning, organizing, and standardizing language to establish a reliable foundation for analysis and interpretation. By removing noise and inconsistencies, preprocessing enhances algorithm performance, leading to more accurate results in tasks such as sentiment analysis, classification, and information retrieval. While the specific workflow will depend on your research question and analytical goals, here is a breakdown of some common steps, along with an example

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Text Data Mining (TDM) is a research process for deriving high-quality information based on insights and patterns from text corpora.

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