def update_revcat_sen_score(cat='gltn'):
"""Update RevCat table with sen_scores"""
# select all revcat entries where cat_code == category and return the review text
results = db.session.query(ReviewCategory.revcat_id,
PlatePalReview.text).join(PlatePalReview)
results_by_cat = results.filter(ReviewCategory.cat_code == cat).all()
# for the list of revcats / review text
for result in results_by_cat:
revcat_id = result[0]
text = result[1]
# note: predict_sentiment components revived in function
sentiment_score = predict_sentiment([text])
# store prediction_list[0][0][2] (decision_function score) as sen_score
sen_score = sentiment_score[0][2]
# update entry in db --> get entire entry from revcat by revcat_id
revcat = db.session.query(ReviewCategory).filter(
ReviewCategory.revcat_id == revcat_id).one()
revcat.sen_score = sen_score
# print "this is revcat.revcat_id", revcat.revcat_id
# print "this is revcat.sen_score", revcat.sen_score
db.session.add(revcat)
db.session.commit()
print "... database updated!"
return
def update_revcat_sen_score(cat='gltn'):
"""Update RevCat table with sen_scores"""
# select all revcat entries where cat_code == category and return the review text
results = db.session.query(ReviewCategory.revcat_id, PlatePalReview.text).join(PlatePalReview)
results_by_cat = results.filter(ReviewCategory.cat_code==cat).all()
# for the list of revcats / review text
for result in results_by_cat:
revcat_id = result[0]
text = result[1]
# note: predict_sentiment components revived in function
sentiment_score = predict_sentiment([text])
# store prediction_list[0][0][2] (decision_function score) as sen_score
sen_score = sentiment_score[0][2]
# update entry in db --> get entire entry from revcat by revcat_id
revcat = db.session.query(ReviewCategory).filter(ReviewCategory.revcat_id==revcat_id).one()
revcat.sen_score = sen_score
# print "this is revcat.revcat_id", revcat.revcat_id
# print "this is revcat.sen_score", revcat.sen_score
db.session.add(revcat)
db.session.commit()
print "... database updated!"
return
def seed_sentcats():
"""
For sentences in Sentences, categorize using multilabel classifier
Add results to SentCats -- Initial seeding version
"""
# select all sentences from Sentences table
results = db.session.query(Sentence).offset(0).all()
# for each sentence, categorize with classifier
for sentence in results:
sent_id = sentence.sent_id
text = sentence.sent_text
predictions = categorize_text(text)
for cat in predictions:
# for 'gltn', perform sentiment analysis
if cat == 'gltn':
# note: predict_sentiment components revived in function
sentiment_score = predict_sentiment([text])
# store prediction_list[0][0][2] (decision_function score) as sen_score
sen_score = sentiment_score[0][2]
# query db to check for entry
sentcat = SentenceCategory.query.filter(
SentenceCategory.sent_id == sent_id).first()
if not sentcat:
sentcat = SentenceCategory(sent_id=sent_id,
cat_code='gltn',
sen_score=sen_score)
else:
sentcat.sen_score = sen_score
else:
# query db to check for entry
sentcat = SentenceCategory.query.filter(
SentenceCategory.sent_id == sent_id).first()
if not sentcat:
# TODO: will have to perform sentiment analysis and update later
sentcat = SentenceCategory(sent_id=sent_id, cat_code=cat)
else:
pass
db.session.add(sentcat)
db.session.commit()
return
def seed_sentcats():
"""
For sentences in Sentences, categorize using multilabel classifier
Add results to SentCats -- Initial seeding version
"""
# select all sentences from Sentences table
results = db.session.query(Sentence).offset(0).all()
# for each sentence, categorize with classifier
for sentence in results:
sent_id = sentence.sent_id
text = sentence.sent_text
predictions = categorize_text(text)
for cat in predictions:
# for 'gltn', perform sentiment analysis
if cat == 'gltn':
# note: predict_sentiment components revived in function
sentiment_score = predict_sentiment([text])
# store prediction_list[0][0][2] (decision_function score) as sen_score
sen_score = sentiment_score[0][2]
# query db to check for entry
sentcat = SentenceCategory.query.filter(SentenceCategory.sent_id==sent_id).first()
if not sentcat:
sentcat = SentenceCategory(sent_id=sent_id,
cat_code='gltn',
sen_score=sen_score)
else:
sentcat.sen_score=sen_score
else:
# query db to check for entry
sentcat = SentenceCategory.query.filter(SentenceCategory.sent_id==sent_id).first()
if not sentcat:
# TODO: will have to perform sentiment analysis and update later
sentcat = SentenceCategory(sent_id=sent_id,
cat_code=cat
)
else:
pass
db.session.add(sentcat)
db.session.commit()
return
