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.'"

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2Preprocessing

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

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

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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]]

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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

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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.