LDA with scikit-learn in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does LDA stand for in topic modeling?

LDA stands for Latent Dirichlet Allocation. It is a method to find hidden topics in a collection of documents.

beginner

What is the main goal of LDA in text analysis?

The main goal of LDA is to discover groups of words (topics) that frequently appear together in documents, helping us understand the themes in a text collection.

intermediate

Which scikit-learn class is used to perform LDA for topic modeling?

The class is sklearn.decomposition.LatentDirichletAllocation. It fits the model to a document-term matrix to find topics.

intermediate

What input format does scikit-learn's LDA expect?

It expects a document-term matrix, usually a sparse matrix where rows are documents and columns are word counts or frequencies.

intermediate

How can you interpret the output of an LDA model in scikit-learn?

The model provides topic-word distributions and document-topic distributions. You can see which words belong to each topic and how much each topic contributes to each document.

What does the 'n_components' parameter specify in sklearn's LDA?

A Number of topics to find

B Number of documents

C Number of words in vocabulary

D Number of iterations

The 'n_components' parameter sets how many topics the model will try to find.

Which data structure is commonly used to represent the input for LDA in scikit-learn?

A List of topics

B Raw text strings

C Document-term matrix

D Word embeddings

LDA requires a document-term matrix where each row is a document and each column is a word count.

What does the 'fit' method do in sklearn's LDA?

A Transforms documents into word counts

B Learns the topic distributions from the data

C Preprocesses the text

D Visualizes the topics

The 'fit' method trains the LDA model to find topics in the input data.

How can you get the topic distribution for a new document after training LDA?

A Use the 'score' method

B Use the 'fit' method again

C Use the 'predict' method

D Use the 'transform' method on the document-term vector

The 'transform' method returns the topic distribution for new documents.

Which of these is NOT a typical step before applying LDA?

A Training a neural network

B Tokenizing text into words

C Converting text to a document-term matrix

D Removing stop words

Training a neural network is not required for LDA, which is a probabilistic model.

Explain how to prepare text data for LDA using scikit-learn.

Think about turning raw text into numbers that LDA can understand.

Describe how to interpret the topics found by LDA in scikit-learn.

Focus on what the model tells you about words and documents.