Topics
¶
Visualize topics, their sizes, and their corresponding words
This visualization is highly inspired by LDAvis, a great visualization technique typically reserved for LDA.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
topic_model |
A fitted BERTopic instance. |
required | |
topics |
List[int] |
A selection of topics to visualize |
None |
top_n_topics |
int |
Only select the top n most frequent topics |
None |
width |
int |
The width of the figure. |
650 |
height |
int |
The height of the figure. |
650 |
Usage:
To visualize the topics simply run:
topic_model.visualize_topics()
Or if you want to save the resulting figure:
fig = topic_model.visualize_topics()
fig.write_html("path/to/file.html")
Source code in bertopic\plotting\_topics.py
def visualize_topics(topic_model,
topics: List[int] = None,
top_n_topics: int = None,
width: int = 650,
height: int = 650) -> go.Figure:
""" Visualize topics, their sizes, and their corresponding words
This visualization is highly inspired by LDAvis, a great visualization
technique typically reserved for LDA.
Arguments:
topic_model: A fitted BERTopic instance.
topics: A selection of topics to visualize
top_n_topics: Only select the top n most frequent topics
width: The width of the figure.
height: The height of the figure.
Usage:
To visualize the topics simply run:
```python
topic_model.visualize_topics()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_topics()
fig.write_html("path/to/file.html")
```
<iframe src="../../getting_started/visualization/viz.html"
style="width:1000px; height: 680px; border: 0px;""></iframe>
"""
# Select topics based on top_n and topics args
freq_df = topic_model.get_topic_freq()
freq_df = freq_df.loc[freq_df.Topic != -1, :]
if topics is not None:
topics = list(topics)
elif top_n_topics is not None:
topics = sorted(freq_df.Topic.to_list()[:top_n_topics])
else:
topics = sorted(freq_df.Topic.to_list())
# Extract topic words and their frequencies
topic_list = sorted(topics)
frequencies = [topic_model.topic_sizes[topic] for topic in topic_list]
words = [" | ".join([word[0] for word in topic_model.get_topic(topic)[:5]]) for topic in topic_list]
# Embed c-TF-IDF into 2D
all_topics = sorted(list(topic_model.get_topics().keys()))
indices = np.array([all_topics.index(topic) for topic in topics])
embeddings = topic_model.c_tf_idf.toarray()[indices]
embeddings = MinMaxScaler().fit_transform(embeddings)
embeddings = UMAP(n_neighbors=2, n_components=2, metric='hellinger').fit_transform(embeddings)
# Visualize with plotly
df = pd.DataFrame({"x": embeddings[1:, 0], "y": embeddings[1:, 1],
"Topic": topic_list[1:], "Words": words[1:], "Size": frequencies[1:]})
return _plotly_topic_visualization(df, topic_list, width, height)