Implements neural topic modeling using deep learning architectures for improved
topic discovery and representation learning.
Usage
run_neural_topics_internal(
texts,
n_topics = 10,
hidden_layers = 2,
hidden_units = 100,
dropout_rate = 0.2,
embedding_model = "all-MiniLM-L6-v2",
seed = 123
)
Arguments
- texts
Character vector of documents
- n_topics
Number of topics to discover
- hidden_layers
Number of hidden layers in neural network
- hidden_units
Number of units per hidden layer
- dropout_rate
Dropout rate for regularization
- embedding_model
Transformer model for initial embeddings
- seed
Random seed for reproducibility
Value
List containing neural topic model and diagnostics
