The course begins with framing analytical questions and exploring analytical tool sets. It progresses through preparing data files, performing word frequency and keyword analysis, and conducting sentiment analysis. Advanced topics include visualizing text data, coding, named entity recognition, and topic recognition in documents. The book also covers text similarity scoring and the analysis of large datasets by sampling.
Throughout this journey, readers will apply the CRISP-DM data mining standard, using companion files with numerous datasets for practical exercises. By the end, participants will have a comprehensive understanding of text analytics, enabling them to derive meaningful insights from textual data to inform business strategies.
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