def word_similarity_graph(self, dataframe, word):
corpus = st.CorpusFromPandas(dataframe, category_col='Document Type',
text_col='Text', nlp=self.nlp).build()
html = word_similarity_explorer(corpus,
category='1st Document',
category_name='1st Document',
not_category_name='2nd Document',
target_term=word,
minimum_term_frequency=5,
pmi_threshold_coefficient=4,
width_in_pixels=1000,
alpha=0.01,
max_p_val=0.05,
save_svg_button=True)
logger.getLogger().info("Opening Word Similarity Visual")
open(self.term_file, 'wb').write(html.encode('utf-8'))
def main():
nlp = spacy.load('en_core_web_sm')
convention_df = SampleCorpora.ConventionData2012.get_data()
corpus = CorpusFromPandas(convention_df,
category_col='party',
text_col='text',
nlp=nlp).build()
html = word_similarity_explorer(corpus,
category='democrat',
category_name='Democratic',
not_category_name='Republican',
target_term='jobs',
minimum_term_frequency=5,
width_in_pixels=1000,
metadata=convention_df['speaker'],
alpha=0.01,
max_p_val=0.1,
save_svg_button=True)
open('./demo_similarity.html', 'wb').write(html.encode('utf-8'))
print('Open ./demo_similarlity.html in Chrome or Firefox.')
def main():
nlp = spacy.load('en')
# ================================================================================
# convention_df = SampleCorpora.ConventionData2012.get_data()
# print("convention_df",convention_df)
# party speaker \
# 0 democrat BARACK OBAMA
# 1 democrat MICHELLE OBAMA
# 2 democrat RICHARD DURBIN
# 3 democrat JOSEPH BIDEN
# 4 democrat JILL BIDEN
# 5 democrat ANGIE FLORES
# text
# 0 Thank you. Thank you. Thank you. Thank you so much.Thank you.Thank you so much. Thank you. Thank you very
# 1 Thank you so much. Tonight, I am so thrilled and so honored and so proud to introduce the love of my life
# 2 Thank you. It is a singular honor to be here tonight. Eight years ago in Boston, I introduced you to a sta
# 3 Hey, Delaware. \nAnd my favorite Democrat, Jilly, I want you to know that Beau and Hunt and Ashley and I —
# 4 Hello. \nThank you, Angie. I'm so proud of how far you've come.\nI'm so proud to stand before you tonight
# 5 My name is Angie Flores and I am a student at Miami-Dade College. \nWhen you grow up in a family where get
# print("convention_df",convention_df.shape)
# (189, 3)
# df1=convention_df.iloc[:10,:]
# df2=convention_df.iloc[150:160,:]
# df_cat=pd.concat([df1,df2],axis=0)
# # print("df_cat",df_cat.shape)
# # (20, 3)
# convention_df=df_cat
# ================================================================================
# convention_df=pd.read_csv('/mnt/1T-5e7/mycodehtml/Data_mining/Visualization/Scattertext/Data/WebMD_Metformin_oral/text.csv',encoding='utf8',error_bad_lines=False)
# print("convention_df",convention_df.shape)
# ================================================================================
# corpus = CorpusFromPandas(convention_df,
# category_col='party',
# text_col='text',
# nlp=nlp).build()
# ================================================================================
all_satisfaction_score_comment_in_all_conds = utils_data.get_all_satisfaction_score_comment_in_all_conds(
)
# print("all_satisfaction_score_comment_in_all_conds",all_satisfaction_score_comment_in_all_conds)
# [['negative', 'Satisfaction', 'after a week----mouth ulccers,cudnt talk,eat,drink for 5 days....whole body burnt,headache, fatigue....quit---am slowly getting better, wudnt give to my worst
# print("all_satisfaction_score_comment_in_all_conds",len(all_satisfaction_score_comment_in_all_conds))
# 1402
# ================================================================================
columns = ['senti_on_Metfor_oral', 'feature', 'review']
all_satisfaction_score_comment_in_all_conds_df = pd.DataFrame(
all_satisfaction_score_comment_in_all_conds,
index=None,
columns=columns)
# ================================================================================
corpus = CorpusFromPandas(all_satisfaction_score_comment_in_all_conds_df,
category_col='senti_on_Metfor_oral',
text_col='review',
nlp=nlp).build()
# ================================================================================
html = word_similarity_explorer(
corpus,
category='negative',
category_name='Negative',
not_category_name='Positive',
target_term='jobs',
minimum_term_frequency=5,
width_in_pixels=1000,
metadata=all_satisfaction_score_comment_in_all_conds_df['feature'],
alpha=0.01,
max_p_val=0.1,
save_svg_button=True)
# ================================================================================
open(
'/mnt/1T-5e7/mycodehtml/Data_mining/Visualization/Scattertext/demo_similarity.html',
'wb').write(html.encode('utf-8'))
print('Open ./demo_similarlity.html in Chrome or Firefox.')