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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Data Preparation
Whether you have collected your own data or will be reusing existing datasets, you probably need to clean them up before you move forward with data analysis. This process includes fixing or removing incorrect, corrupted, unformatted, duplicate, or incomplete data. While the cleaning-up process may look different depending on the dataset you have at hand, this handout covers some essential tips to complete this task more efficiently while making your data more consistent, accurate, and high quality.