topic-presence.html

{% extends "layout.html" %}

{% block main %}
<main class="main">
    <div class="main_content">
        <h1>Relative presence of all topics in the corpus</h1>
        <p>This overview page is the entry point to exploring the text corpus. The topics are ranked by their relative
            presence in the whole corpus and display each in a bar with width proportional to the topic’s presence
            score. You can navigate through your corpus by clicking on the topics.</p>
        {% for topic, proportion in presence %}
        <p><a href="{{ url_for('topics', topic=topic) }}"><button class="topic" style="width: {{ proportion }}%;">{{ topic }}</button></a></p>
        {% endfor %}
        <blockquote>Mathematically, the topic model has two goals in explaining the
            documents. First, it wants its topics to place high probability on few terms. Second, it wants to attach
            documents
            to as few topics as possible. These goals are at odds. With few terms assigned to each topic, the model
            captures the
            observed words by using more topics per article. With few topics assigned to each article, the model
            captures the
            observed words by using more terms per topic.<footer>
                <cite>
                    <a href="http://journalofdigitalhumanities.org/2-1/topic-modeling-and-digital-humanities-by-david-m-blei/">David
                        M. Blei</a>
                </cite>
            </footer>
        </blockquote>
    </div>
</main>
{% endblock %}