Choosing number of topics in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library needed for topic modeling.

NLP

from sklearn.decomposition import [1]

Drag options to blanks, or click blank then click option'

APCA

BKMeans

CNMF

DTSNE

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing PCA or KMeans which are not used for topic modeling here.

The NMF class from sklearn.decomposition is used for topic modeling by factorizing the document-term matrix.

2fill in blank

medium

Complete the code to create an NMF model with a specified number of topics.

NLP

model = NMF(n_components=[1], random_state=42)

Drag options to blanks, or click blank then click option'

A5

B0.5

C'five'

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using a float or string instead of an integer for number of topics.

The n_components parameter expects an integer number of topics, such as 5.

3fill in blank

hard

Fix the error in the code to fit the NMF model on the document-term matrix X.

NLP

W = model.[1](X)

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Afit_predict

Bfit_transform

Ctransform

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without fitting first causes an error.

The fit_transform method fits the model and returns the topic distribution matrix W.

4fill in blank

hard

Fill both blanks to compute reconstruction error and print it.

NLP

error = model.[1]
print('Reconstruction error:', model.[2])

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Areconstruction_err_

Breconstruction_error_

Creconstruction_err

Dreconstruction_error

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect attribute names like reconstruction_error or missing underscore.

The attribute reconstruction_err_ stores the reconstruction error after fitting the model.

5fill in blank

hard

Fill all three blanks to create a dictionary of topic words with their weights for the first topic.

NLP

topic_words = {word: model.components_[0, [1]] for [2], word in enumerate(vectorizer.get_feature_names_out()) if [3] < 10}

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Ai

Bidx

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing variable names or using wrong variable names for indices.

We use idx as the index to access weights, idx as the index variable in enumeration, and filter words with index less than 10.

Practice

1. Why is it important to choose the right number of topics in topic modeling?

easy

A. To find clear and meaningful groups in the text data

B. To make the model run faster regardless of quality

C. To reduce the size of the text documents

D. To avoid using any stop words in the text

Solution

1. Step 1: Understand the goal of topic modeling

   Topic modeling groups similar words and documents into topics to find hidden themes.

2. Step 2: Importance of topic number choice

   Choosing the right number of topics helps get clear, meaningful groups instead of too broad or too many confusing topics.

3. Final Answer:

   To find clear and meaningful groups in the text data -> Option A

4. Quick Check:

   Right topic number = clear groups [OK]

Hint: Right topic count = clear groups, not too few or many [OK]

Common Mistakes:

• Thinking speed is the main reason to choose topic number
• Believing topic number reduces document size
• Confusing stop words removal with topic number choice

2. Which of the following is the correct way to set the number of topics in a typical LDA model using Python's gensim library?

easy

A. lda_model = LdaModel(corpus, n_topics=5, id2word=dictionary)

B. lda_model = LdaModel(corpus, topics=5, id2word=dictionary)

C. lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary)

D. lda_model = LdaModel(corpus, topic_number=5, id2word=dictionary)

Solution

1. Step 1: Recall gensim LDA parameter names

   The correct parameter to set number of topics is num_topics.

2. Step 2: Check each option

   Only lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary) uses num_topics=5, others use incorrect parameter names.

3. Final Answer:

   lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary) -> Option C

4. Quick Check:

   Parameter name for topics = num_topics [OK]

Hint: Use 'num_topics' parameter to set topic count in gensim LDA [OK]

Common Mistakes:

• Using 'topics' or 'n_topics' instead of 'num_topics'
• Confusing parameter names from other libraries
• Omitting the id2word dictionary parameter

3. Given the following code snippet using sklearn's NMF for topic modeling, what will be the shape of the matrix W if n_components=4 and the input X has shape (100, 500)?

from sklearn.decomposition import NMF
model = NMF(n_components=4, random_state=42)
W = model.fit_transform(X)

medium

A. (4, 4)

B. (4, 500)

C. (100, 500)

D. (100, 4)

Solution

1. Step 1: Understand NMF output matrices

   NMF factorizes X (samples x features) into W (samples x components) and H (components x features).

2. Step 2: Apply shapes to given data

   X shape is (100, 500), n_components=4, so W shape is (100, 4).

3. Final Answer:

   (100, 4) -> Option D

4. Quick Check:

   W shape = samples x components = (100, 4) [OK]

Hint: W shape = number of samples by number of topics/components [OK]

Common Mistakes:

• Confusing W with H matrix shape
• Mixing up rows and columns in matrix shapes
• Assuming output shape equals input shape

4. You ran LDA with num_topics=10 but found many topics have very similar top words. What is the likely issue and how to fix it?

medium

A. Too few topics chosen; increase num_topics to get more variety

B. Too many topics chosen; reduce num_topics to get clearer topics

C. Stop words were not removed; remove stop words to fix

D. The dictionary is too small; add more words to dictionary

Solution

1. Step 1: Analyze similar topics with many overlaps

   If many topics share similar top words, it means topics are not distinct enough, often due to too many topics.

2. Step 2: Adjust number of topics

   Reducing num_topics helps merge similar topics into clearer, distinct groups.

3. Final Answer:

   Too many topics chosen; reduce num_topics to get clearer topics -> Option B

4. Quick Check:

   Similar topics = too many topics [OK]

Hint: Similar topics? Try fewer topics for clarity [OK]

Common Mistakes:

• Increasing topics when topics are already too similar
• Blaming stop words without checking topic overlap
• Adding words to dictionary without checking topic count

5. You have a large collection of news articles and want to find topics. You try 3, 5, 10, and 20 topics. The 3-topic model groups articles too broadly, and the 20-topic model creates many overlapping topics. How should you decide the best number of topics?

hard

A. Choose the number that balances clear, distinct topics without too much overlap, often between 5 and 10

B. Always pick the highest number of topics for more detail

C. Pick the lowest number of topics to keep it simple

D. Randomly select a number since topic modeling is unsupervised

Solution

1. Step 1: Understand the trade-off in topic numbers

   Too few topics cause broad groups; too many cause overlap and confusion.

2. Step 2: Choose a balanced number

   Testing multiple values and selecting one with clear, distinct topics (often between extremes) is best practice.

3. Final Answer:

   Choose the number that balances clear, distinct topics without too much overlap, often between 5 and 10 -> Option A

4. Quick Check:

   Balance topic count for clarity and detail [OK]

Hint: Balance topic count: not too few, not too many [OK]

Common Mistakes:

• Always picking max topics without checking overlap
• Choosing too few topics ignoring broadness
• Ignoring evaluation of topic quality