demo_custom_topic_model.py

import scattertext as st

topic_model = {
    'money': ['money', 'bank', 'banks', 'finances', 'financial', 'loan', 'dollars', 'income'],
    'jobs': ['jobs', 'workers', 'labor', 'employment', 'worker', 'employee', 'job'],
    'patriotic': ['america', 'country', 'flag', 'americans', 'patriotism', 'patriotic'],
    'family': ['mother', 'father', 'mom', 'dad', 'sister', 'brother', 'grandfather', 'grandmother', 'son', 'daughter']
}

convention_df = st.SampleCorpora.ConventionData2012.get_data().assign(
    parse=lambda df: df['text'].apply(st.whitespace_nlp_with_sentences)
)

topic_feature_builder = st.FeatsFromTopicModel(topic_model)

topic_corpus = st.CorpusFromParsedDocuments(
    convention_df,
    category_col='party',
    parsed_col='parse',
    feats_from_spacy_doc=topic_feature_builder
).build()

print(topic_corpus._X.shape, topic_corpus._mX.shape, )

topic_corpus = st.CorpusFromPandas(
    convention_df,
    category_col='party',
    text_col='text',
    nlp=st.whitespace_nlp_with_sentences,
    feats_from_spacy_doc=topic_feature_builder
).build()

print(topic_corpus._X.shape, topic_corpus._mX.shape, )


html = st.produce_scattertext_explorer(
    topic_corpus,
    category='democrat',
    category_name='Democratic',
    not_category_name='Republican',
    width_in_pixels=1000,
    metadata=convention_df['speaker'],
    use_non_text_features=True,
    use_full_doc=True,
    pmi_threshold_coefficient=0,
    topic_model_term_lists=topic_feature_builder.get_top_model_term_lists()
)

open('./demo_custom_topic_model.html', 'wb').write(html.encode('utf-8'))
print('Open ./demo_custom_topic_model.html in Chrome or Firefox.')