Function visualize_term_rank

bertopic/plotting/_term_rank.py:6–131

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 w

(
    topic_model,
    topics: List[int] | None = None,
    log_scale: bool = False,
    custom_labels: Union[bool, str] = False,
    title: str = "<b>Term score decline per Topic</b>",
    width: int = 800,
    height: int = 500,
)

def visualize_term_rank(
    topic_model,
    topics: List[int] | None = None,
    log_scale: bool = False,
    custom_labels: Union[bool, str] = False,
    title: str = "<b>Term score decline per Topic</b>",
    width: int = 800,
    height: int = 500,
) -> go.Figure:
    """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")
    ```

    <iframe src="../../getting_started/visualization/term_rank.html"
    style="width:1000px; height: 530px; border: 0px;""></iframe>

    <iframe src="../../getting_started/visualization/term_rank_log.html"
    style="width:1000px; height: 530px; border: 0px;""></iframe>

    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