Latent Dirichlet Allocation (LDA) in NLP - Model Pipeline Trace

Model Pipeline - Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA) is a way to find hidden topics in a bunch of text documents. It looks at words and groups them into topics, helping us understand what the texts are about without reading each one.

Data Flow - 5 Stages

1Input Documents

1000 documents x variable length→Raw text documents collected for analysis→1000 documents x variable length

Document 1: 'Cats are great pets.' Document 2: 'The stock market is volatile.'

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2Text Preprocessing

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

['cats', 'great', 'pets'], ['stock', 'market', 'volatile']

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3Create Document-Term Matrix

1000 documents x list of tokens→Count how many times each word appears in each document→1000 documents x 5000 unique words

Doc 1: {'cats':2, 'pets':1}, Doc 2: {'stock':1, 'market':1}

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4LDA Model Training

1000 documents x 5000 words→Fit LDA to find 10 topics with word distributions→10 topics x 5000 words (topic-word distributions)

Topic 1: {'cats':0.1, 'pets':0.08, 'dogs':0.07}, Topic 2: {'stock':0.12, 'market':0.1}

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5Topic Distribution per Document

1000 documents x 5000 words→Calculate topic proportions for each document→1000 documents x 10 topics

Doc 1: [0.7, 0.1, 0.05, ...], Doc 2: [0.05, 0.8, 0.03, ...]

Training Trace - Epoch by Epoch

12000 +
      |
11000 |  *
      |10000 |   *
      | 9000 |    *
      | 8000 |     *
      | 7000 |      **
      +----------------
       1  2  3  4  5  epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	12000.0	N/A	Initial model with random topic assignments
2	9500.0	N/A	Loss decreased as topics start to form
3	8000.0	N/A	Topics become more coherent
4	7200.0	N/A	Model converging, loss decreasing steadily
5	7000.0	N/A	Small improvement, model stabilizing

Prediction Trace - 4 Layers

Layer 1: Input Document Tokens

Layer 2: Topic Distribution Calculation

Layer 3: Topic-Word Probabilities

Layer 4: Final Topic Assignment

Model Quiz - 3 Questions

What does the 'Document-Term Matrix' stage do?

A Assigns topics to documents randomly

B Counts how often each word appears in each document

C Removes stopwords from the text

D Converts topics into words

Key Insight

LDA helps us discover hidden themes in text by grouping words into topics. As training progresses, the model improves topic clarity, shown by decreasing loss. Each document is then described by a mix of these topics, helping us understand large text collections easily.