DTM

Visualize topics over time

Parameters:

Name	Type	Description	Default
topic_model		A fitted BERTopic instance.	required
topics_over_time	DataFrame	The topics you would like to be visualized with the corresponding topic representation	required
top_n_topics	int	To visualize the most frequent topics instead of all	None
topics	List[int]	Select which topics you would like to be visualized	None
normalize_frequency	bool	Whether to normalize each topic's frequency individually	False
custom_labels	Union[bool, str]	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".	False
title	str	Title of the plot.	'<b>Topics over Time</b>'
width	int	The width of the figure.	1250
height	int	The height of the figure.	450

Returns:

Type	Description
Figure	A plotly.graph_objects.Figure including all traces

Examples:

To visualize the topics over time, simply run:

topics_over_time = topic_model.topics_over_time(docs, timestamps)
topic_model.visualize_topics_over_time(topics_over_time)

Or if you want to save the resulting figure:

fig = topic_model.visualize_topics_over_time(topics_over_time)
fig.write_html("path/to/file.html")

Source code in bertopic\plotting\_topics_over_time.py

def visualize_topics_over_time(topic_model,
                               topics_over_time: pd.DataFrame,
                               top_n_topics: int = None,
                               topics: List[int] = None,
                               normalize_frequency: bool = False,
                               custom_labels: Union[bool, str] = False,
                               title: str = "<b>Topics over Time</b>",
                               width: int = 1250,
                               height: int = 450) -> go.Figure:
    """ Visualize topics over time

    Arguments:
        topic_model: A fitted BERTopic instance.
        topics_over_time: The topics you would like to be visualized with the
                          corresponding topic representation
        top_n_topics: To visualize the most frequent topics instead of all
        topics: Select which topics you would like to be visualized
        normalize_frequency: Whether to normalize each topic's frequency individually
        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:
        A plotly.graph_objects.Figure including all traces

    Examples:

    To visualize the topics over time, simply run:

    ```python
    topics_over_time = topic_model.topics_over_time(docs, timestamps)
    topic_model.visualize_topics_over_time(topics_over_time)
    ```

    Or if you want to save the resulting figure:

    ```python
    fig = topic_model.visualize_topics_over_time(topics_over_time)
    fig.write_html("path/to/file.html")
    ```
    <iframe src="../../getting_started/visualization/trump.html"
    style="width:1000px; height: 680px; border: 0px;""></iframe>
    """
    colors = ["#E69F00", "#56B4E9", "#009E73", "#F0E442", "#D55E00", "#0072B2", "#CC79A7"]

    # 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:
        selected_topics = list(topics)
    elif top_n_topics is not None:
        selected_topics = sorted(freq_df.Topic.to_list()[:top_n_topics])
    else:
        selected_topics = sorted(freq_df.Topic.to_list())

    # Prepare data
    if isinstance(custom_labels, str):
        topic_names = [[[str(topic), None]] + topic_model.topic_aspects_[custom_labels][topic] for topic in topics]
        topic_names = ["_".join([label[0] for label in labels[:4]]) for labels in topic_names]
        topic_names = [label if len(label) < 30 else label[:27] + "..." for label in topic_names]
        topic_names = {key: topic_names[index] for index, key in enumerate(topic_model.topic_labels_.keys())}
    elif topic_model.custom_labels_ is not None and custom_labels:
        topic_names = {key: topic_model.custom_labels_[key + topic_model._outliers] for key, _ in topic_model.topic_labels_.items()}
    else:
        topic_names = {key: value[:40] + "..." if len(value) > 40 else value
                       for key, value in topic_model.topic_labels_.items()}
    topics_over_time["Name"] = topics_over_time.Topic.map(topic_names)
    data = topics_over_time.loc[topics_over_time.Topic.isin(selected_topics), :].sort_values(["Topic", "Timestamp"])

    # Add traces
    fig = go.Figure()
    for index, topic in enumerate(data.Topic.unique()):
        trace_data = data.loc[data.Topic == topic, :]
        topic_name = trace_data.Name.values[0]
        words = trace_data.Words.values
        if normalize_frequency:
            y = normalize(trace_data.Frequency.values.reshape(1, -1))[0]
        else:
            y = trace_data.Frequency
        fig.add_trace(go.Scatter(x=trace_data.Timestamp, y=y,
                                 mode='lines',
                                 marker_color=colors[index % 7],
                                 hoverinfo="text",
                                 name=topic_name,
                                 hovertext=[f'<b>Topic {topic}</b><br>Words: {word}' for word in words]))

    # Styling of the visualization
    fig.update_xaxes(showgrid=True)
    fig.update_yaxes(showgrid=True)
    fig.update_layout(
        yaxis_title="Normalized Frequency" if normalize_frequency else "Frequency",
        title={
            'text': f"{title}",
            'y': .95,
            'x': 0.40,
            'xanchor': 'center',
            'yanchor': 'top',
            'font': dict(
                size=22,
                color="Black")
        },
        template="simple_white",
        width=width,
        height=height,
        hoverlabel=dict(
            bgcolor="white",
            font_size=16,
            font_family="Rockwell"
        ),
        legend=dict(
            title="<b>Global Topic Representation",
        )
    )
    return fig