In text mining, a topic is defined as a set of words frequently occurring together:

Question

BlackTom AI · Accepted Answer

To evaluate the statement, we need to consider what a 'topic' means in text mining.
Option 1: True. In many text-mining contexts, a topic is associated with words that tend to appear together across documents. This reflects the idea that a topic captures a semantic theme or concept, manifested by a cluster of co-occurring terms. While a topic in formal models like LDA is technically a probability distribution over the vocabulary, the intuition that a topic corresponds to a set of words frequently appearing together is a reasonable shorthand for describing the theme and its characteristic terms.
Option 2: False. If we interpret a topic strictly as more than just a collection of co-occurring words, we might emphasize that a topic is not merely a fixed set of words but a probabilistic distribution over all words, indicating the strength or weight of each word's association with the topic. Moreover, topics are typically defined across documents and can influence the probability of words appearing in various contexts, not just a raw co-occurrence snapshot. This interpretation highlights that a topic embodies statistical relationships, not just a static list of terms.

Taken together, the statement leans on a commonly used intuition in text mining, though a rigorous definition points to a probabilistic topic model rather than a simple set of frequent words. The distinction matters for understanding how topics are learned and applied in practice.

In text mining, a topic is defined as a set of words frequently occurring together:判断题

类似问题

The output of the Latent Dirichlet Allocation (LDA) topic model is:

The Latent Dirichlet Allocation (LDA) topic model is a generative model, that is it mimics (but in a simplified way) how humans write documents:

Who decides on the number of topics in the Latent Dirichlet Allocation (LDA) topic model?

The Latent Dirichlet Allocation (LDA) topic model needs an initial guess for the distribution of topics in documents and distribution of words in each topic:

The Latent Dirichlet Allocation (LDA) topic model accepts data in the following format (choose one best answer):

The topics are latent in the Latent Dirichlet Allocation topic model and therefore have to be inferred from the data:

The Latent Dirichlet Allocation (LDA) approach to topic modeling assumes that:

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