When analyzing text data in Tableau, which feature would best support detailed examination?

The best feature for supporting a detailed examination when analyzing text data in Tableau is text splitting. This function allows users to take a textual field and break it down into multiple components based on specific delimiters or patterns. For instance, if you have a field containing full names, text splitting can separate them into first names and last names. This granularity helps in better understanding patterns, conducting sentiment analysis, or extracting useful insights from the text data.

Text splitting is particularly essential in text analysis as it enables deeper exploration of the data by allowing users to manipulate and analyze substrings or specific elements of the text. This can lead to more meaningful visualizations and interactions with the data, particularly when trying to uncover trends or themes that might be obscured in the original text format.

Other options, although useful in different contexts, do not provide the same level of detailed examination for text data specifically. Data blending and data filtering are more focused on combining or selectively displaying data, while aggregation is centered around summarizing numerical data rather than dissecting text for detailed insight.