Choosing number of topics in NLP - Model Pipeline Trace

Model Pipeline - Choosing number of topics

This pipeline helps us find the best number of topics for a topic model. It starts with text data, cleans and prepares it, then tries different numbers of topics. We check how well each model fits the data and pick the best number.

Data Flow - 6 Stages

1Raw Text Data

1000 documents x variable length→Collect raw text documents→1000 documents x variable length

"Document 1: 'Cats are great pets.'"

↓

2Preprocessing

1000 documents x variable length→Lowercase, remove stopwords, tokenize→1000 documents x list of tokens

[['cats', 'great', 'pets'], ['dogs', 'friendly']]

↓

3Feature Engineering

1000 documents x list of tokens→Create document-term matrix (DTM)→1000 documents x 5000 unique words

[[0,1,0,...,2], [1,0,0,...,0]]

↓

4Model Training with different topic numbers

1000 documents x 5000 words→Train LDA models with k=2 to k=10 topics→Models with k topics each

Model with 5 topics trained

↓

5Model Evaluation

Models with k topics→Calculate coherence score for each model→Coherence scores for k=2 to 10

k=5 topics: coherence=0.45

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6Select Best Number of Topics

Coherence scores for k=2 to 10→Choose k with highest coherence→Selected number of topics k=5

Best k=5 with coherence=0.45

Training Trace - Epoch by Epoch

Loss
1.2 |*       
1.0 | *      
0.85|  *     
0.75|   *    
0.78|    *   
0.80|     *  
0.85|      * 
0.90|       *
0.95|        *
    +---------
     2 3 4 5 6 7 8 9 10 Topics

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	N/A	Initial model with 2 topics, loss high
2	1.0	N/A	Model with 3 topics, loss decreased
3	0.85	N/A	Model with 4 topics, better fit
4	0.75	N/A	Model with 5 topics, loss lowest so far
5	0.78	N/A	Model with 6 topics, slight loss increase
6	0.80	N/A	Model with 7 topics, loss increased
7	0.85	N/A	Model with 8 topics, loss increased more
8	0.90	N/A	Model with 9 topics, loss higher
9	0.95	N/A	Model with 10 topics, loss highest

Prediction Trace - 3 Layers

Layer 1: Input Document

Layer 2: Document-Term Matrix Vectorization

Layer 3: Topic Distribution Prediction

Model Quiz - 3 Questions

Why do we train models with different numbers of topics?

A To find the number that best groups the documents

B To make the model run faster

C To reduce the number of words in documents

D To increase the document length

Key Insight

Choosing the right number of topics balances detail and clarity. Too few topics mix ideas; too many split them too much. Using coherence scores and loss helps find the best number for meaningful topics.

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