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Prompt

You mentioned doing topic cluster analytic. How might I do that to possibly map the progression of my interests over time? I assume that would be possible given that the creation data is part of the recorded file metadata

Output

To perform topic cluster analysis on your dataset of GPT outputs in order to map the progression of your interests over time, you can use a combination of natural language processing (NLP) techniques, machine learning models, and visualization tools. Given that you have creation dates as part of the recorded file metadata, you could track how your interests have evolved by analyzing topic clusters from different time periods.

Here’s a detailed step-by-step guide on how you could accomplish this:

Step 1: Preprocessing the Dataset

  1. Extract Text Data and Metadata:
  2. Extract the text of each GPT output along with the corresponding creation date from your Obsidian vault.
  3. Convert the data into a structured format (e.g., CSV or JSON) where each record has fields for text, date, and any relevant metadata.
  4. Text Cleaning:
  5. Remove stopwords (common words like "the," "and," etc.), punctuation, and special characters that do not contribute meaningfully to the content.
  6. Tokenize the text to prepare it for further analysis.

Step 2: Topic Modeling

  1. Choose a Topic Modeling Algorithm:
  2. Latent Dirichlet Allocation (LDA): This is a popular method for discovering abstract topics in a collection of text. LDA can group words that frequently occur together, helping to define topics.
  3. Non-negative Matrix Factorization (NMF): NMF is also used for topic modeling and can be effective for text analysis.
  4. BERTopic: A newer and powerful topic modeling technique that uses transformer-based embeddings (such as BERT) and clustering algorithms like HDBSCAN. It is especially effective for capturing nuanced topics.
  5. Vectorize the Text:
  6. Use TF-IDF (Term Frequency-Inverse Document Frequency) or transformer embeddings (e.g., BERT or sentence-transformers) to represent the text in numerical form.
  7. TF-IDF is often used for LDA, while embeddings from models like BERT work well with clustering-based methods like BERTopic.
  8. Fit the Topic Model:
  9. Run the chosen algorithm (e.g., LDA or BERTopic) on your dataset to identify topics.
  10. With LDA, you will need to specify the number of topics upfront, while BERTopic can automatically determine the number of topics based on the data.
  11. Label the Topics:
  12. Manually review the top words from each topic to assign meaningful labels. The labels will represent the high-level themes of the conversations.

Step 3: Time-Based Topic Analysis

  1. Map Topics Over Time:
  2. Create time bins (e.g., by month or quarter) using the creation date metadata.
  3. Assign each output to a time bin and aggregate the topics within each bin to determine which topics were dominant during specific periods.
  4. Use BERTopic if you’d like the topic model to incorporate time-awareness and map the evolution more fluidly.
  5. Generate Time Series Data:
  6. For each topic, calculate its frequency (number of outputs assigned to that topic) in each time bin.
  7. This gives you a time series dataset for each topic that you can visualize.

Step 4: Visualize Topic Evolution

  1. Stacked Area Chart:
  2. Use a stacked area chart to visualize the progression of different topics over time. This helps you see which topics gained or lost interest over specific periods.
  3. You could use tools like Matplotlib, Seaborn, or Plotly in Python to create these visualizations.
  4. Topic Trend Line:
  5. Plot a line chart for each topic to show the change in its prevalence over time. This can help you see trends in your interests, such as topics that peaked during specific times and those that have become more consistent.
  6. Interactive Dashboards:
  7. For a more interactive exploration, consider building a dashboard using Power BI, Tableau, or Plotly Dash.
  8. A dashboard can allow you to filter by different time periods, drill down into specific topics, or even visualize co-occurrence relationships between topics over time.

Step 5: Deriving Insights

  1. Identify Shifts in Interests:
  2. Examine the visualizations to see which topics became more or less frequent over time. This can show clear shifts in your focus, which may reflect changes in your personal interests, the progression of your projects, or even broader trends that influenced you.
  3. Cross-Topic Analysis:
  4. Identify correlations between different topics. For example, you might notice that an increase in a specific technical topic is correlated with a decrease in a general interest topic, suggesting a shift in focus from exploration to specialization.
  5. Cluster Progression:
  6. Use hierarchical clustering to identify how smaller subtopics are evolving within larger themes. This could show whether an interest is becoming more focused or if it’s diversifying into new areas.

Example Python Workflow Using BERTopic

Below is an example of how you might use BERTopic for this type of analysis:

from bertopic import BERTopic
import pandas as pd
import matplotlib.pyplot as plt

# Load your dataset into a DataFrame
df = pd.read_csv('gpt_outputs.csv')  # Assuming 'gpt_outputs.csv' contains 'text' and 'date' columns

# Convert the 'date' column to datetime
df['date'] = pd.to_datetime(df['date'])

# Initialize and fit the BERTopic model
topic_model = BERTopic()
topics, _ = topic_model.fit_transform(df['text'])

# Assign topics back to the DataFrame
df['topic'] = topics

# Aggregate topics over time
df['month'] = df['date'].dt.to_period('M')  # Group by month
topic_trends = df.groupby(['month', 'topic']).size().reset_index(name='count')

# Plot topic evolution
topic_model.visualize_topics_over_time(df'text', 'date')
plt.show()

Tools and Libraries to Consider

  • Python Libraries:
  • BERTopic: A powerful library for topic modeling and visualizing topic evolution.
  • Gensim: To perform LDA topic modeling.
  • Pandas: To handle the data preparation and aggregation.
  • Matplotlib / Seaborn / Plotly: For visualizing topic trends over time.
  • Interactive Tools:
  • Tableau or Power BI: For more detailed and interactive visualization of topic trends.
  • Plotly Dash: To create interactive web-based visualizations.

Summary

Using topic cluster analysis, you can effectively map the evolution of your interests over time by following these steps:

  • Preprocess and clean your dataset.
  • Use topic modeling algorithms like LDA or BERTopic to group similar content.
  • Visualize topic evolution by creating time series charts to understand the changes in frequency and importance of topics over time.
  • Use interactive tools for deeper exploration, allowing you to gain insights into trends and correlations between different areas of interest.

This approach will allow you to create a dynamic visual representation of how your focus areas have changed over time, giving you a clearer understanding of your intellectual journey and helping identify any emerging themes or patterns in your work.