Package 'bertopicr'

Configure Homebrew zlib on macOS

Description

Sets DYLD_FALLBACK_LIBRARY_PATH to Homebrew's zlib lib directory. This can help reticulate find compatible libraries on macOS.

Usage

configure_macos_homebrew_zlib(quiet = FALSE)

Arguments

quiet

Logical. If TRUE, suppresses messages.

Value

Logical. TRUE if the environment was updated, FALSE otherwise.

---

Find Topics DataFrame Function

Description

This function finds the most similar topics to given keywords using a BERTopic model and returns the results in a data frame or tibble format.

Usage

find_topics_df(model, queries, top_n = 10, return_tibble = TRUE)

Arguments

model	A BERTopic model object. Must be passed from the calling environment.
queries	A vector of keywords or phrases to query the topics for.
top_n	Number of top similar topics to retrieve for each query. Default is 10.
return_tibble	Logical. If TRUE, returns a tibble. If FALSE, returns a data.frame. Default is TRUE.

Value

A data.frame or tibble with columns for the keyword, topics, and similarity scores for each query.

Examples

# Example of finding similar topics using a BERTopic model
if (exists("topic_model")) {
  queries <- c("national minority", "minority issues", "nationality issues")
  find_topics_df(model = topic_model, queries = queries, top_n = 10)
} else {
  message("No topic_model found. Please load a BERTopic model and try again.")
}

---

Get Document Information DataFrame

Description

This function retrieves document information from a BERTopic model and processes it to unnest list columns, replace NA values, and consolidate columns with the same prefix.

Usage

get_document_info_df(model, texts, drop_expanded_columns = TRUE)

Arguments

model	A BERTopic model object.
texts	A character vector containing the preprocessed texts to be passed to the BERTopic model.
drop_expanded_columns	Logical. If TRUE, drops the expanded columns after consolidation. Default is TRUE.

Value

A data.frame or tibble with unnested and consolidated columns.

Examples

## Not run: 
document_info_df <- get_document_info_df(model = topic_model,
texts = texts_cleaned, drop_expanded_columns = TRUE)
print(document_info_df)

## End(Not run)

---

Get Most Representative Documents for a Specific Topic

Description

This function filters a given data frame to select the most representative documents for a specified topic based on their probability scores. The documents are sorted by relevance in descending order, and the top n documents are returned.

Usage

get_most_representative_docs(df, topic_nr, n_docs = 5)

Arguments

df	A data frame containing at least the columns 'Topic', 'Document', and 'probs'.
topic_nr	An integer specifying the topic number to filter the documents.
n_docs	An integer specifying the number of top representative documents to return. Defaults to 5.

Value

A vector of the most representative documents corresponding to the specified topic. If the number of documents available is less than n_docs, all available documents are returned.

Examples

## Not run: 
# Assuming `df_docs` is a data frame with columns `Topic`, `Document`, and `probs`
get_most_representative_docs(df_docs, topic_nr = 3, n_docs = 5)

## End(Not run)

---

Get Representative Documents for a Specific Topic

Description

This function filters a given data frame to select a specified number of representative documents from a particular topic. It uses random sampling to select the documents.

Usage

get_representative_docs_custom(df, topic_nr, n_docs)

Arguments

df	A data frame containing at least the columns 'Topic' and 'Document'.
topic_nr	An integer specifying the topic number to filter the documents.
n_docs	An integer specifying the number of documents to sample for the specified topic.

Value

A vector of sampled documents corresponding to the specified topic.

Examples

## Not run: 
# Assuming `df_docs` is a data frame with columns `Topic`, `Document`, and `probs`
get_representative_docs_custom(df_docs, topic_nr = 3, n_docs = 5)

## End(Not run)

---

Get Topic DataFrame Function

Description

This function retrieves a specified number of words with high probability for a given topic number from a BERTopic model and returns the results in a data frame or tibble format.

Usage

get_topic_df(model, topic_number = 0, top_n = 10, return_tibble = TRUE)

Arguments

model	A BERTopic model object. Must be passed from the calling environment.
topic_number	The topic number for which words and scores are retrieved.
top_n	Number of top words to retrieve for the specified topic. Default is 10. If greater than 10, it will be set to 10 as BERTopic returns a maximum of 10 words.
return_tibble	Logical. If TRUE, returns a tibble. If FALSE, returns a data.frame. Default is TRUE.

Value

A data.frame or tibble with columns for the word, score, and topic number.

Examples

## Not run: 
# Example usage:
if (exists("topic_model")) {
  topic_df <- get_topic_df(model = topic_model, topic_number = 3, top_n = 5)
  print(topic_df)
} else {
  message("No topic_model found. Please load a BERTopic model and try again.")
}

## End(Not run)

---

Get Topic Information DataFrame

Description

This function retrieves topic information from a BERTopic model and processes it to unnest list columns, replace NA values, and consolidate columns with the same prefix.

Usage

get_topic_info_df(model, drop_expanded_columns = TRUE)

Arguments

model	A BERTopic model object.
drop_expanded_columns	Logical. If TRUE, drops the expanded columns after consolidation. Default is TRUE.

Value

A data.frame or tibble with unnested and consolidated columns.

Examples

## Not run: 
topic_info_df <- get_topic_info_df(model = topic_model,
drop_expanded_columns = TRUE)
print(topic_info_df)

## End(Not run)

---

Get Topics DataFrame Function

Description

This function retrieves all topics from a BERTopic model and converts them into a data frame or tibble format.

Usage

get_topics_df(model, return_tibble = TRUE)

Arguments

model	A BERTopic model object. Must be passed from the calling environment.
return_tibble	Logical. If TRUE, returns a tibble. If FALSE, returns a data.frame. Default is TRUE.

Value

A data.frame or tibble with columns for the word, score, and topic number across all topics.

Examples

## Not run: 
topics_df <- get_topics_df(model = topic_model)
print(topics_df)

## End(Not run)

---

Load a BERTopic Model Bundle

Description

Load a BERTopic model saved with save_bertopic_model() along with its companion RDS file containing R-side extras.

Usage

load_bertopic_model(path, embedding_model = NULL)

Arguments

path	Directory path where the Python model was saved.
embedding_model	Optional embedding model to pass through to BERTopic$load() when the embedding model is not serialized.

Value

A list with two elements: model (the BERTopic model) and extras (the R-side data saved in the companion RDS file).

Examples

## Not run: 
loaded <- load_bertopic_model("topic_model")
doc_info <- get_document_info_df(model = loaded$model, texts = docs)

## End(Not run)

---

Save a BERTopic Model Bundle

Description

Persist a trained BERTopic model to disk and store R-side extras in a companion RDS file. This is the recommended way to reuse a model across sessions when working through reticulate.

Usage

save_bertopic_model(topic_model, path)

Arguments

topic_model	A list returned by train_bertopic_model(). Must contain a Python BERTopic model at topic_model$model. Optional extras such as probabilities, reduced embeddings, topics over time, or topics per class are saved when present and set to NULL otherwise.
path	Directory path to write the Python model to. The RDS companion file is saved as paste0(path, "_extras.rds").

Value

Invisibly returns TRUE after successful write.

Examples

## Not run: 
save_bertopic_model(topic_model, "topic_model")

## End(Not run)

---

Set Up Python Environment for BERTopic

Description

This function sets up a Python environment with all required packages for using the BERTopic model within the R package. It can create and activate a virtualenv or conda environment and then install the bundled requirements.

Usage

setup_python_environment(
  envname = "r-bertopic",
  python_path = NULL,
  method = c("virtualenv", "conda"),
  python_version = NULL,
  upgrade = TRUE,
  extra_packages = NULL
)

Arguments

envname	The name of the Python environment. Default is "r-bertopic".
python_path	Optional path to a specific Python executable (virtualenv only).
method	Environment type to create and use. One of "virtualenv" or "conda".
python_version	Optional Python version for conda (e.g. "3.10").
upgrade	Logical. If TRUE, passes –upgrade to pip installs. Default is TRUE.
extra_packages	Optional character vector of additional Python packages to install.

Value

Invisibly returns the active Python configuration.

---

Train a BERTopic Model

Description

This function creates embeddings with sentence-transformers, configures UMAP, HDBSCAN, and CountVectorizer, optionally wires a representation model, and fits a BERTopic model from R. The returned model can be used with bertopicr helpers.

Usage

train_bertopic_model(
  docs,
  embedding_model = "Qwen/Qwen3-Embedding-0.6B",
  embeddings = NULL,
  embedding_batch_size = 32,
  embedding_show_progress = TRUE,
  umap_model = NULL,
  umap_n_neighbors = 15,
  umap_n_components = 5,
  umap_min_dist = 0,
  umap_metric = "cosine",
  umap_random_state = 42,
  hdbscan_model = NULL,
  hdbscan_min_cluster_size = 50,
  hdbscan_min_samples = 20,
  hdbscan_metric = "euclidean",
  hdbscan_cluster_selection_method = "eom",
  hdbscan_gen_min_span_tree = TRUE,
  hdbscan_prediction_data = TRUE,
  hdbscan_core_dist_n_jobs = 1,
  vectorizer_model = NULL,
  stop_words = "all_stopwords",
  ngram_range = c(1, 3),
  min_df = 2L,
  max_df = 50L,
  max_features = 10000,
  strip_accents = NULL,
  decode_error = "strict",
  encoding = "UTF-8",
  representation_model = c("none", "keybert", "mmr", "ollama"),
  representation_params = list(),
  ollama_model = NULL,
  ollama_base_url = "http://localhost:11434/v1",
  ollama_api_key = "ollama",
  ollama_client_params = list(),
  ollama_prompt = NULL,
  top_n_words = 200L,
  calculate_probabilities = TRUE,
  verbose = TRUE,
  seed = NULL,
  timestamps = NULL,
  topics_over_time_nr_bins = 20L,
  topics_over_time_global_tuning = TRUE,
  topics_over_time_evolution_tuning = TRUE,
  classes = NULL,
  compute_reduced_embeddings = TRUE,
  reduced_embedding_n_neighbors = 10L,
  reduced_embedding_min_dist = 0,
  reduced_embedding_metric = "cosine",
  compute_hierarchical_topics = TRUE,
  bertopic_args = list()
)

Arguments

docs	Character vector of documents to model.
embedding_model	Sentence-transformers model name or local path.
embeddings	Optional precomputed embeddings (matrix or array).
embedding_batch_size	Batch size for embedding encoding.
embedding_show_progress	Logical. Show embedding progress bar.
umap_model	Optional pre-built UMAP Python object. If NULL, one is created.
umap_n_neighbors	Number of neighbors for UMAP.
umap_n_components	Number of UMAP components.
umap_min_dist	UMAP min_dist parameter.
umap_metric	UMAP metric.
umap_random_state	Random state for UMAP.
hdbscan_model	Optional pre-built HDBSCAN Python object. If NULL, one is created.
hdbscan_min_cluster_size	HDBSCAN min_cluster_size.
hdbscan_min_samples	HDBSCAN min_samples.
hdbscan_metric	HDBSCAN metric.
hdbscan_cluster_selection_method	HDBSCAN cluster selection method.
hdbscan_gen_min_span_tree	HDBSCAN gen_min_span_tree.
hdbscan_prediction_data	Logical. Whether to generate prediction data.
hdbscan_core_dist_n_jobs	HDBSCAN core_dist_n_jobs.
vectorizer_model	Optional pre-built CountVectorizer Python object.
stop_words	Stop words for CountVectorizer. Use "all_stopwords" to load the bundled multilingual list, "english", or a character vector.
ngram_range	Length-2 integer vector for n-gram range.
min_df	Minimum document frequency for CountVectorizer.
max_df	Maximum document frequency for CountVectorizer.
max_features	Maximum features for CountVectorizer.
strip_accents	Passed to CountVectorizer. Use NULL to preserve umlauts.
decode_error	Passed to CountVectorizer when decoding input bytes.
encoding	Text encoding for CountVectorizer (defaults to "utf-8").
representation_model	Representation model to use: "none", "keybert", "mmr", or "ollama".
representation_params	Named list of parameters passed to the representation model.
ollama_model	Ollama model name when representation_model = "ollama".
ollama_base_url	Base URL for the Ollama OpenAI-compatible endpoint.
ollama_api_key	API key placeholder for the Ollama OpenAI-compatible endpoint.
ollama_client_params	Named list of extra parameters passed to openai$OpenAI().
ollama_prompt	Optional prompt template for the Ollama OpenAI representation.
top_n_words	Number of top words per topic to keep in the model.
calculate_probabilities	Logical. Whether to calculate topic probabilities.
verbose	Logical. Verbosity for BERTopic.
seed	Optional random seed.
timestamps	Optional vector of timestamps (Date/POSIXt/ISO strings or integer) for topics over time. Defaults to NULL (topics over time disabled).
topics_over_time_nr_bins	Number of bins for topics_over_time.
topics_over_time_global_tuning	Logical. Whether to enable global tuning for topics_over_time.
topics_over_time_evolution_tuning	Logical. Whether to enable evolution tuning for topics_over_time.
classes	Optional vector of class labels (character or factor) for topics per class. Defaults to NULL (topics per class disabled).
compute_reduced_embeddings	Logical. If TRUE, computes 2D and 3D UMAP reductions.
reduced_embedding_n_neighbors	Number of neighbors for reduced embeddings.
reduced_embedding_min_dist	UMAP min_dist for reduced embeddings.
reduced_embedding_metric	UMAP metric for reduced embeddings.
compute_hierarchical_topics	Logical. If TRUE, computes hierarchical topics.
bertopic_args	Named list of extra arguments passed to BERTopic().

Value

A list with elements model, topics, probabilities, embeddings, reduced_embeddings_2d, reduced_embeddings_3d, hierarchical_topics, topics_over_time, and topics_per_class.

Examples

## Not run: 
setup_python_environment()
texts <- c("Cats are great pets", "Dogs are loyal companions", "Markets fluctuate")
fit <- train_bertopic_model(texts, embedding_model = "sentence-transformers/all-MiniLM-L6-v2")
visualize_topics(fit$model, filename = "intertopic_distance_map", auto_open = FALSE)

## End(Not run)

---

Visualize BERTopic Bar Chart

Description

This function visualizes the topics of a BERTopic model using Plotly and saves the output as an interactive HTML file. It checks for required Python modules and allows for custom file naming.

Usage

visualize_barchart(
  model,
  filename = "topics_topwords_interactive_barchart",
  open_file = FALSE
)

Arguments

model	A BERTopic model object. Must be passed from the calling environment.
filename	A character string specifying the name of the HTML file to save the bar chart. Default is "topics_topwords_interactive_barchart". The .html extension is added automatically if not provided.
open_file	Logical. If TRUE, opens the HTML file after saving. Default is FALSE.

Value

Displays the interactive bar chart within the R environment and saves it as an HTML file.

Examples

## Not run: 
visualize_barchart(model = topic_model, filename = "custom_barchart",
open_file = TRUE)

## End(Not run)

---

Visualize Topic Distribution for a Specific Document using BERTopic

Description

This function visualizes the topic distribution for a specific document from a BERTopic model using Python's Plotly library. The visualization is saved as an interactive HTML file, which can be opened and viewed in a web browser.

Usage

visualize_distribution(
  model,
  text_id = 1,
  probabilities,
  filename = "topic_dist_interactive",
  auto_open = FALSE
)

Arguments

model	A BERTopic model object. The model must have the method visualize_distribution.
text_id	An integer specifying the index of the document for which the topic distribution is visualized. Default is 1. Must be a positive integer and a valid index within the probabilities matrix.
probabilities	A matrix or data frame of topic probabilities, with rows corresponding to documents and columns to topics. Each element represents the probability of a topic for a given document.
filename	A character string specifying the name of the HTML file to save the visualization. Default is "topic_dist_interactive". The .html extension will be added automatically.
auto_open	Logical. If TRUE, the HTML file will automatically open in the browser. Default is FALSE.

Value

The function does not return a value but saves an HTML file containing the visualization and displays it in the current R environment.

Examples

## Not run: 
# Assuming 'topic_model' is a BERTopic model object and 'probs' is a matrix of topic probabilities
visualize_distribution(
  model = topic_model,
  text_id = 1,
  probabilities = probs,
  filename = "custom_filename",
  auto_open = TRUE)

## End(Not run)

---

Visualize Documents in Reduced Embedding Space

Description

This function generates a visualization of documents using a pre-trained BERTopic model. It uses UMAP to reduce the dimensionality of embeddings and Plotly for interactive visualizations.

Usage

visualize_documents(
  model = topic_model,
  texts = texts_cleaned,
  reduced_embeddings = reduced_embeddings,
  custom_labels = FALSE,
  hide_annotation = TRUE,
  filename = "visualize_documents",
  auto_open = FALSE
)

Arguments

model	A BERTopic model object. Default is 'topic_model'.
texts	A list or vector of cleaned text documents to visualize. Default is 'texts_cleaned'.
reduced_embeddings	A matrix of reduced-dimensionality embeddings. Typically generated using UMAP. Default is 'reduced_embeddings'.
custom_labels	A logical value indicating whether to use custom labels for topics. Default is FALSE.
hide_annotation	A logical value indicating whether to hide annotations in the plot. Default is TRUE.
filename	A string specifying the name of the HTML file to save the visualization. Default is "visualize_documents".
auto_open	A logical value indicating whether to automatically open the HTML file after saving. Default is FALSE.

Value

A Plotly visualization of the documents, displayed as an HTML file within the R environment.

Examples

## Not run: 
visualize_documents(model = topic_model,
                    texts = texts_cleaned,
                    reduced_embeddings = reduced_embeddings,
                    custom_labels = FALSE,
                    hide_annotation = TRUE,
                    filename = "visualize_documents",
                    auto_open = FALSE)

## End(Not run)

---

Visualize Documents in 2D Space using BERTopic

Description

This function generates a 3D visualization of documents using a pre-trained BERTopic model and UMAP dimensionality reduction. It uses Plotly for interactive visualizations and saves the output as an HTML file.

Usage

visualize_documents_2d(
  model,
  texts,
  reduced_embeddings,
  custom_labels = FALSE,
  hide_annotation = TRUE,
  tooltips = c("Topic", "Name", "Probability", "Text"),
  filename = "visualize_documents_2d",
  auto_open = FALSE
)

Arguments

model	A BERTopic model object. Default is 'topic_model'.
texts	A character vector or list of cleaned text documents to visualize.
reduced_embeddings	A matrix or data frame of reduced-dimensionality embeddings (2D). Typically generated using UMAP.
custom_labels	Logical. If TRUE, custom topic labels are used. Default is FALSE.
hide_annotation	Logical. If TRUE, hides annotations on the plot. Default is TRUE.
tooltips	A character vector of tooltips for hover information. Default is c("Topic", "Name", "Probability", "Text").
filename	A character string specifying the name of the HTML file to save the visualization. Default is "visualize_documents_2d". The .html extension is automatically added if not provided.
auto_open	Logical. If TRUE, opens the HTML file in the browser after saving. Default is FALSE.

Value

The function does not return a value but saves an HTML file containing the visualization and displays it in the current R environment.

Examples

## Not run: 
visualize_documents_2d(model = topic_model,
  texts = texts_cleaned,
  reduced_embeddings = embeddings,
  custom_labels = FALSE,
  hide_annotation = TRUE,
  filename = "plot",
  auto_open = TRUE)

## End(Not run)

---

Visualize Documents in 3D Space using BERTopic

Description

This function generates a 3D visualization of documents using a pre-trained BERTopic model and UMAP dimensionality reduction. It uses Plotly for interactive visualizations and saves the output as an HTML file.

Usage

visualize_documents_3d(
  model,
  texts,
  reduced_embeddings,
  custom_labels = FALSE,
  hide_annotation = TRUE,
  tooltips = c("Topic", "Name", "Probability", "Text"),
  filename = "visualize_documents_3d",
  auto_open = FALSE
)

Arguments

model	A BERTopic model object. Default is 'topic_model'.
texts	A character vector or list of cleaned text documents to visualize.
reduced_embeddings	A matrix or data frame of reduced-dimensionality embeddings (3D). Typically generated using UMAP.
custom_labels	Logical. If TRUE, custom topic labels are used. Default is FALSE.
hide_annotation	Logical. If TRUE, hides annotations on the plot. Default is TRUE.
tooltips	A character vector of tooltips for hover information. Default is c("Topic", "Name", "Probability", "Text").
filename	A character string specifying the name of the HTML file to save the visualization. Default is "visualize_documents_3d". The .html extension is automatically added if not provided.
auto_open	Logical. If TRUE, opens the HTML file in the browser after saving. Default is FALSE.

Value

The function does not return a value but saves an HTML file containing the visualization and displays it in the current R environment.

Examples

## Not run: 
visualize_documents_3d(model = topic_model,
  texts = texts_cleaned,
  reduced_embeddings = embeddings,
  custom_labels = FALSE,
  hide_annotation = TRUE,
  filename = "plot",
  auto_open = TRUE)

## End(Not run)

---

Visualize Topic Similarity Heatmap using BERTopic

Description

This function visualizes the topic similarity heatmap of topics from a BERTopic model using Python's Plotly library. The visualization is saved as an interactive HTML file, which can be opened and viewed in a web browser.

Usage

visualize_heatmap(
  model,
  filename = "topics_similarity_heatmap",
  auto_open = FALSE
)

Arguments

model	A BERTopic model object. The model must have the method visualize_heatmap.
filename	A character string specifying the name of the HTML file to save the visualization. The default value is "topics_similarity_heatmap". The filename should not contain illegal characters. The .html extension is added automatically if not provided.
auto_open	Logical. If TRUE, opens the HTML file after saving. Default is FALSE.

Value

The function does not return a value but saves an HTML file containing the visualization and displays it in the current R environment.

Examples

## Not run: 
# Assuming 'topic_model' is a BERTopic model object
visualize_heatmap(model = topic_model, filename = "topics_similarity_heatmap", auto_open = FALSE)

## End(Not run)

---

Visualize Topic Hierarchy Nodes using BERTopic

Description

This function visualizes the hierarchical clustering of topics from a BERTopic model. If a hierarchical topics DataFrame is provided, it uses this for visualization; otherwise, it visualizes directly from the model. The visualization is saved as an interactive HTML file, which can be opened and viewed in a web browser.

Usage

visualize_hierarchy(
  model,
  hierarchical_topics = NULL,
  filename = "topic_hierarchy",
  auto_open = TRUE
)

Arguments

model	A BERTopic model object. The model must have the method visualize_hierarchy.
hierarchical_topics	Optional. A hierarchical topics DataFrame created using the BERTopic model's hierarchical_topics method. If provided, this object is used to generate the hierarchy visualization.
filename	A character string specifying the name of the HTML file to save the visualization. The default value is "topic_hierarchy". The filename should not contain illegal characters.
auto_open	Logical. If TRUE, the HTML file will be opened automatically after being saved. Default is TRUE.

Value

The function does not return a value but saves an HTML file containing the visualization and displays it in the current R environment.

Examples

## Not run: 
# Assuming 'topic_model' is a BERTopic model object
visualize_hierarchy(model = topic_model, filename = "topic_hierarchy",
auto_open = TRUE)

# Alternatively, provide a pre-calculated hierarchical_topics object
visualize_hierarchy(model = topic_model,
hierarchical_topics = hierarchical_topics,
filename = "topic_hierarchy",
auto_open = TRUE)

## End(Not run)

---

Visualize Topics using BERTopic

Description

This function visualizes the intertopic distance map of topics from a BERTopic model using Python's Plotly library. The visualization is saved as an interactive HTML file, which can be opened and viewed in a web browser.

Usage

visualize_topics(
  model,
  filename = "intertopic_distance_map",
  auto_open = FALSE
)

Arguments

model	A BERTopic model object. The model must have the method visualize_topics.
filename	A character string specifying the name of the HTML file to save the visualization. The default value is "intertopic_distance_map". The filename should not contain illegal characters. The .html extension is added automatically if not provided.
auto_open	Logical. If TRUE, opens the HTML file after saving. Default is FALSE.

Value

The function does not return a value but saves an HTML file containing the visualization and displays it in the current R environment.

Examples

## Not run: 
# Assuming 'topic_model' is a BERTopic model object
visualize_topics(model = topic_model, filename = "plot", auto_open = TRUE)

## End(Not run)

---

Visualize Topics Over Time using BERTopic

Description

This function visualizes topics over time from a BERTopic model using Python's Plotly library. The visualization is saved as an interactive HTML file, which can be opened and viewed in a web browser.

Usage

visualize_topics_over_time(
  model,
  topics_over_time_model,
  top_n_topics = 20,
  filename = "topics_over_time"
)

Arguments

model	A BERTopic model object. The model must have the method visualize_topics_over_time.
topics_over_time_model	A topics-over-time model object created using the BERTopic model.
top_n_topics	An integer specifying the number of top topics to display in the visualization. Default is 20. Must be a positive integer.
filename	A character string specifying the name of the HTML file to save the visualization. The default value is "topics_over_time". The filename should not contain illegal characters.

Value

The function does not return a value but saves an HTML file containing the visualization and displays it in the current R environment.

Examples

## Not run: 
# Assuming 'topics_over_time_model' is a BERTopic model object
visualize_topics_over_time(model = topic_model,
                           topics_over_time_model = topics_over_time,
                           top_n_topics = 5,
                           filename = "plot")

## End(Not run)

---

Visualize Topics per Class

Description

This function visualizes the distribution of topics per class using a pre-trained BERTopic model. The visualization is generated using the Plotly Python package and displayed within an R environment.

Usage

visualize_topics_per_class(
  model = topic_model,
  topics_per_class = topics_per_class,
  start = 0,
  end = 10,
  filename = "topics_per_class",
  auto_open = TRUE
)

Arguments

model	A BERTopic model object. Default is 'topic_model'.
topics_per_class	A data frame or list containing the topics per class data. Default is 'topics_per_class'.
start	An integer specifying the starting index of the topics to visualize. Default is 0.
end	An integer specifying the ending index of the topics to visualize. Default is 10.
filename	A string specifying the name of the HTML file to save the visualization. Default is "topics_per_class".
auto_open	A logical value indicating whether to automatically open the HTML file after saving. Default is TRUE.

Value

A Plotly visualization of the topics per class, displayed as an HTML file within the R environment.

Examples

## Not run: 
visualize_topics_per_class(model = topic_model,
                           topics_per_class = topics_per_class,
                           start = 0, end = 7,
                           filename = "plot",
                           auto_open = TRUE)

## End(Not run)

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