Which of the following techniques is most suitable for handling and organizing an unstructured dataset with textual data?

Text parsing and tokenization are crucial steps for processing unstructured textual data. Parsing involves extracting and structuring data from text, while tokenization breaks down text into meaningful elements or "tokens" for analysis. This approach is particularly useful for unstructured datasets like customer reviews, social media comments, or any free-form text where content analysis is required. By structuring the data through tokenization, a data analyst can perform further analysis, like sentiment analysis or topic modeling, to extract insights from textual data. The other options are incorrect because: • Linear Regression is a statistical technique, unsuitable for unstructured text. • Data Normalization standardizes numeric values, not text. • Data Aggregation consolidates data, but doesn't handle text processing specifically. • K-means Clustering groups data, but tokenization is first needed for textual data.