When analyzing articles, the tf-idf-tf_idf framework is used to:

Question

BlackTom AI · Accepted Answer

To analyze how tf-idf-tf_idf works, we need to consider what the metric is designed to do across a collection of documents.
Option 1: 'identify tokens or terms that are most frequent to each article' – While term frequency within a document can be high for some words, tf-idf specifically downplays terms that are merely frequent across many documents and emphasizes terms that are distinctive for that document. So this option captures only part of the picture and can be misleading as a sole criterion.
Option 2: 'identify tokens or terms that are most important/specific to each article' – This aligns with the core idea of tf-idf: a term that is relatively important to a document because it has high frequency in that document but is not common across the entire corpus (hence more specific to that article). This statement correctly conveys the emphasis on discriminative, document-specific terms.
Option 3: 'identify tokens or terms that are both, most frequent and most important/specific to each article' – This combines the two notions: a term should be prominent within the article (high TF) and relatively rare in other articles (high IDF), yielding terms that are both frequent in the document and informative for distinguishing it. This is a reasonable description of what tf-idf aims to surface.
In terms of potential misconceptions, if one focuses solely on frequency across the whole dataset, one might miss terms that are highly informative but not globally common. Conversely, considering only rarity without document prominence would ignore terms that are genuinely representative of the document content. The tf-idf framework specifically balances these aspects to highlight distinctive terms.
Therefore, the statements that capture tf-idf’s purpose are the ones describing terms that are important/specific to each article, and those that are both frequent within the article and informative across the corpus. The option that asserts only frequency without considering distinctiveness does not fully describe tf-idf’s goal.

Business Analysis with Unstructured Data - DAT-7471 - BMBAN2 In-class knowledge check #2 (Remotely Proctored)

When analyzing articles, the tf-idf-tf_idf framework is used to:

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