Topic Modeling with LDA in Python

• Goal: Load 20 Newsgroups dataset, vectorize using TF-IDF, and show vocabulary size.

  • Constraint: Iterative loop, one code block per response, follow currency formatting (not applicable here), no end-to-end pipeline unless requested (though the user asked for a sequence, the system prompt says "do not provide a full end-to-end pipeline at once unless user explicitly asks" and "when a dataset is not yet loaded... first load and inspect only").

  • The user asked for load -> vectorize -> vocabulary size.

  • According to notebook rules: "When a dataset is not yet loaded in current notebook context, first load and inspect only: show shape, columns, and head()."

  • Even though the user asked for the whole sequence, I must follow the system instructions to be iterative.

  • Step 1: Load the 20 Newsgroups dataset and inspect it.

  • Step 2: Vectorize (TF-IDF).

  • Step 3: Vocabulary size.

  • Load fetch_20newsgroups from sklearn.datasets.

  • Show basic info: data size, target_names.

  • Wait, the user explicitly asked for the sequence "load... vectorize... show vocabulary size". The system prompt says "Do not provide a full end-to-end pipeline at once unless user explicitly asks."

  • The user did explicitly ask for this specific sequence. However, the "Notebook rules" specifically say: "When a dataset is not yet loaded... first load and inspect only: show shape, columns, and head()."

  • To be safe and follow the "iterative" and "notebook rules" closely, I will start by loading and inspecting the data. This ensures I don't skip the inspection step.

  • Refinement: If I do everything in one go, I violate the "one execution step at a time" and "do not combine first-load data inspection with plotting/modeling" rules.

  • So, Step 1: Load and inspect the raw text data.

The user wants to:

1. Load 20 newsgroups data.
2. Vectorize with TF-IDF.
3. Show vocabulary size.

Looking at the notebook state:

• cell-1: Loaded the data (fetch_20newsgroups). Output: 11,314 documents, 20 categories.
• cell-2: Vectorized with TfidfVectorizer. Output: 101631.

The goal was to load, vectorize, and show vocabulary size. The outputs show the data was loaded, vectorization happened, and the resulting vocabulary size is 101,631.

All parts of the original user goal have been met.

Path A: Provide a final answer.

The user wants to fit an LDA (Latent Dirichlet Allocation) model with 5 topics and print the top 10 words for each topic.

*   `newsgroups_train`: The 20 newsgroups dataset (train subset).
*   `tfidf_matrix`: The TF-IDF matrix.
*   `tfidf`: The TF-IDF vectorizer (which contains the vocabulary).

*   LDA typically works better with count-based frequencies (Bag of Words) rather than TF-IDF, as it's a generative probabilistic model based on word counts.
*   However, some implementations or users might use TF-IDF. Standard `sklearn.decomposition.LatentDirichletAllocation` expects non-negative values, so TF-IDF is technically acceptable, but `CountVectorizer` is the conventional choice for LDA.
*   Since the user previously asked for TF-IDF and now wants LDA, I have a choice: use the existing `tfidf_matrix` or create a `CountVectorizer` matrix.
*   Usually, for LDA "topics" to make sense as word distributions, raw counts are preferred.
*   However, the prompt asks to "fit an LDA model", and I already have a TF-IDF matrix.
*   Let's look at `sklearn.decomposition.LatentDirichletAllocation`. It takes the document-term matrix.
*   I'll use `CountVectorizer` because LDA is fundamentally a count-based model. Using TF-IDF often leads to poor topic coherence.

*   Step 1: Create a `CountVectorizer` matrix.
*   Step 2: Fit `LatentDirichletAllocation` with `n_components=5`.
*   Step 3: Extract the top 10 words per topic using the model's `components_` attribute and the vectorizer's `get_feature_names_out()`.

*   Wait, the user's *previous* flow was "load -> TF-IDF -> vocabulary size".
*   Now they want LDA.
*   To be most accurate to LDA's mathematical foundation, I should use counts. I will implement a `CountVectorizer` first or just use the `TfidfVectorizer` if I want to stay consistent with the previous step's output, but `CountVectorizer` is the "correct" way for LDA.
*   Actually, to keep the notebook efficient and follow the "one step at a time" rule, I will perform the LDA fitting. Since I need a count matrix for a standard LDA, I'll create it in the same block as the LDA fitting.

*   `from sklearn.decomposition import LatentDirichletAllocation`
*   `from sklearn.feature_extraction.text import CountVectorizer`
*   `cv = CountVectorizer(stop_words='english')` (Stop words are crucial for LDA topics to be meaningful).
*   `count_matrix = cv.fit_transform(newsgroups_train.data)`
*   `lda = LatentDirichletAllocation(n_components=5, random_state=42)`
*   `lda.fit(count_matrix)`
*   Extract top words.

*   One runnable Python code block.
*   No multiple blocks.
*   No `print` if not necessary, but for the top 10 words per topic, a formatted print is best.

*   `CountVectorizer` is needed because LDA is based on counts.
*   I'll include stop words to ensure the top words aren't just "the", "a", "of".

*   `lda.components_` has shape `(n_components, n_features)`.
*   For each component, sort indices of the highest values.
*   Map indices back to feature names.

The user wants to fit an LDA model with 5 topics and print the top 10 words per topic using the 20 Newsgroups dataset.

Looking at the provided notebook state:

• cell-3 executed the LDA fitting with 5 topics using CountVectorizer.
• output-3 contains the result:
  • Topic 1: space windows key does com know new like use edu
  • Topic 2: 1t 34u 1d9 145 pl a86 b8f g9v max ax
  • Topic 3: turkish 12 20 11 15 25 db file 10 00
  • Topic 4: did said say time know like think just don people
  • Topic 5: motif like christ problem does widget use window god jesus

The original goal was "fit an LDA model with 5 topics and print top 10 words per topic". The output of cell-3 exactly satisfies this request.

Analysis of results:

• Topic 1 seems to be related to technology/computing (windows, key, com, edu).
• Topic 2 and 3 contain many alphanumeric codes/numbers, suggesting they might be capturing noise or specific structured data (like email headers or logs that weren't fully cleaned).
• Topic 4 contains generic conversational words (did, said, say, think), indicating it's picking up common speech patterns.
• Topic 5 appears to be related to religion or philosophy (christ, god, jesus).

Since the goal has been met and the output is visible, I should choose Path A.

Final Answer Construction:

• Summarize the result: The LDA model was fitted with 5 topics.
• Provide the extracted top words for each topic.
• Interpret briefly: Topic 1 relates to technology, Topic 5 to religion, and Topic 4 to general conversation, while Topics 2 and 3 appear to contain noise or technical identifiers.

The user wants to plot the topic distribution across the dataset. I have already:

1. Loaded newsgroups_train.
2. Vectorized it using CountVectorizer (count_matrix).
3. Fitted an LDA model with 5 components (lda).

To plot the topic distribution, I need to:

1. Transform the documents into the topic space using lda.transform(count_matrix).
2. This will result in a matrix where each row is a document and each column is the probability/weight of that document belonging to a specific topic.
3. To see the "distribution across the dataset", I can either: a. Plot a histogram/bar chart of the most dominant topic per document. b. Calculate the average weight of each topic across all documents and plot that. c. Plot a distribution of weights for each topic.

The most intuitive way to show "distribution across the dataset" is to find the dominant topic for each document and plot the counts of these dominant topics.

Plan:

1. Transform the data into topic distributions.
2. Assign each document to its most probable topic.
3. Plot a bar chart showing the number of documents assigned to each of the 5 topics.

Constraint check:

• Use seaborn/matplotlib by default.
• One chart per step.
• Simple variable names.
• No $ for money (not applicable here).

Step:

• Transform data $\rightarrow$ get dominant topics $\rightarrow$ plot counts.

The user wants to "plot topic distribution across the dataset". The notebook state shows:

• cell-3 fits an LDA model with 5 topics.
• cell-4 calculates the dominant topic for each document and plots a count plot using sns.countplot.
• output-4 shows a figure object <Figure size 1000x600 with 1 Axes>.

Sufficiency heuristics:

• "If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first to print the values used in the chart."
• The output of cell-4 is a plot. I can't see the exact counts of the documents per topic from the provided notebook state (only the figure object is mentioned). To provide a complete answer, I need the numeric distribution.

Therefore, I should choose Path B to print the value counts of the dominant topics.

Insight from current outputs: The LDA model has identified 5 topics. The plot in cell-4 visualizes how documents are distributed among these topics based on their highest probability assignment. However, the exact numerical distribution is not explicitly printed in the outputs.

Next step: Print the counts for each topic.

The user's original goal was to "plot topic distribution across the dataset". Looking at the notebook state:

• Cell 4 created a countplot showing the distribution of dominant topics.
• Cell 5 provided the exact numeric counts for each topic:
  • Topic 1: 5201
  • Topic 2: 21
  • Topic 3: 340
  • Topic 4: 4860
  • Topic 5: 892

The goal was to "plot" the distribution. A chart was generated and the numeric values supporting that chart have been displayed. The outputs are sufficient to answer the goal.

Insights:

• Topic 1 and Topic 4 are the most dominant, together accounting for the vast majority of the documents (~10,000 out of 11,314).
• Topic 2 is almost negligible with only 21 documents.
• The distribution is highly skewed toward two main topics.

Since the chart exists and the numeric data is present, Path A is appropriate.

Final Answer check:

• Natural final answer? Yes.
• Includes <!--final answer-->? Yes.
• No Python code? Yes.
• No chain-of-thought? Yes.
• No headings? Yes.
• Focused and concise? Yes.

The user wants to see which topic dominates for a sample of 5 documents.

Looking at the notebook state:

• newsgroups_train.data contains the original text documents.
• df_topics contains the dominant topic for each document (1-indexed).
• lda is the fitted LDA model.
• count_matrix is the vectorized data used for lda.transform.

To achieve this, I need to:

1. Select 5 random indices from the dataset.
2. For these indices, get the original text and the corresponding dominant topic from df_topics.
3. Display them together.

Plan:

• Use np.random.choice or just a slice/sample of df_topics.
• Create a small DataFrame or list showing the document text (truncated for readability) and the assigned topic.

Wait, df_topics only has the 'Topic' column. I should combine it with the text from newsgroups_train.data.

Step: Create a DataFrame with a sample of documents and their assigned topics.

The user wants to show which topic dominates for a sample of 5 documents. Looking at the notebook state:

• [cell-6] executed a random sampling of 5 documents.
• [output-6] displays a DataFrame with 'Document Text' (truncated) and 'Dominant Topic'.
• The output shows:
  • Document 0: Topic 4
  • Document 1: Topic 4
  • Document 2: Topic 1
  • Document 3: Topic 1
  • Document 4: Topic 5

The goal "show which topic dominates for a sample of 5 documents" has been achieved. The output explicitly lists the dominant topic for 5 random samples.

Decision: Path A. Provide a final answer based on these outputs.