Content:

You want to analyze topics across 12-14 interview questions gathered from ~50 interviews conducted with various stakeholders, including project developers, labor organizations, workforce development organizations, and community-based organizations. The dataset is unevenly distributed among the stakeholders.

Given the nature of the analysis, BERTopic in Python is an excellent choice for this task. BERTopic is a powerful topic modeling library that uses transformer-based embeddings, enabling it to identify nuanced topics even in uneven datasets. Below is an outline of how you can approach this analysis:
Steps for Topic Analysis:

    Data Preprocessing:
        Load and clean the text data.
        Combine responses from all interviews for each question if necessary.
        Annotate each response with the corresponding stakeholder type.

    Embedding Creation:
        Use BERTopic's default model or a domain-specific language model (e.g., Sentence-BERT).
        Generate embeddings for the interview responses.

    Topic Modeling:
        Use BERTopic to identify topics across the dataset.
        Analyze the resulting topics for interpretability and coherence.

    Stakeholder Comparison:
        Group responses by stakeholder type.
        Apply BERTopic within each stakeholder group to identify unique or shared topics.
        Compare the topic distributions to identify differences across groups.

    Visualization:
        Use BERTopic’s visualization tools to generate:
            Topic frequency plots.
            Intertopic distance maps.
            Stakeholder-specific topic distributions.

Key Features of BERTopic for This Analysis:

    Dynamic Topic Reduction: Refine topics for interpretability by merging similar ones.
    Custom Embeddings: Incorporate domain-specific knowledge by using pre-trained models relevant to your field.
    Visualization Tools: Gain insights through interactive visualizations of topics and stakeholder comparisons.
Considerations:

    Imbalanced Dataset: You may need to account for uneven distribution by oversampling smaller groups or using techniques like importance weighting.
    Interpretability: After modeling, manually validate the topics to ensure they are meaningful and relevant.
    Fine-Tuning: Experiment with BERTopic’s hyperparameters, such as the embedding model and vectorizer, to optimize results for your dataset.

This framework should provide meaningful insights into the topics across your interviews and highlight differences between stakeholder groups effectively.