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w c c}w )(al Visualize the ranks of all terms across all topics.
Each topic is represented by a set of words. These words, however,
do not all equally represent the topic. This visualization shows
how many words are needed to represent a topic and at which point
the beneficial effect of adding words starts to decline.
Arguments:
topic_model: A fitted BERTopic instance.
topics: A selection of topics to visualize. These will be colored
red where all others will be colored black.
log_scale: Whether to represent the ranking on a log scale
custom_labels: If bool, whether to use custom topic labels that were defined using
`topic_model.set_topic_labels`.
If `str`, it uses labels from other aspects, e.g., "Aspect1".
title: Title of the plot.
width: The width of the figure.
height: The height of the figure.
Returns:
fig: A plotly figure
Examples:
To visualize the ranks of all words across
all topics simply run:
```python
topic_model.visualize_term_rank()
```
Or if you want to save the resulting figure:
```python
fig = topic_model.visualize_term_rank()
fig.write_html("path/to/file.html")
```
Reference:
This visualization was heavily inspired by the
"Term Probability Decline" visualization found in an
analysis by the amazing [tmtoolkit](https://tmtoolkit.readthedocs.io/).
Reference to that specific analysis can be found
[here](https://wzbsocialsciencecenter.github.io/tm_corona/tm_analysis.html).
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