import scattertext as st
import scattertext.interface.ProduceScattertextExplorer
convention_df = st.SampleCorpora.ConventionData2012.get_data()
corpus = (st.CorpusFromPandas(
convention_df,
category_col='speaker',
text_col='text',
nlp=st.whitespace_nlp_with_sentences).build().get_unigram_corpus())
html = scattertext.interface.ProduceScattertextExplorer.produce_scattertext_explorer(
corpus,
category='BARACK OBAMA',
sort_by_dist=False,
metadata=convention_df['party'] + ': ' + convention_df['speaker'],
term_scorer=st.RankDifference(),
transform=st.Scalers.dense_rank)
file_name = 'demo_dense_rank_difference.html'
open(file_name, 'wb').write(html.encode('utf-8'))
print('Open ./%s in Chrome.' % (file_name))
import scattertext as st
movie_df = st.SampleCorpora.RottenTomatoes.get_data()
movie_df.category = movie_df.category.apply\
(lambda x: {'rotten': 'Negative', 'fresh': 'Positive', 'plot': 'Plot'}[x])
corpus = st.CorpusFromPandas(movie_df,
category_col='category',
text_col='text',
nlp=st.whitespace_nlp_with_sentences).build()
corpus = corpus.get_unigram_corpus()
semiotic_square = st.SemioticSquare(corpus,
category_a='Positive',
category_b='Negative',
neutral_categories=['Plot'],
scorer=st.RankDifference())
html = st.produce_semiotic_square_explorer(
semiotic_square,
category_name='Positive',
not_category_name='Negative',
x_label='Fresh-Rotten',
y_label='Plot-Review',
neutral_category_name='Plot Description',
metadata=movie_df['movie_name'])
fn = 'demo_semiotic.html'
open(fn, 'wb').write(html.encode('utf-8'))
print('Open ' + fn + ' in Chrome or Firefox.')