def perform_sentiment_analysis():
with session_scope() as s:
tweets = s.query(Tweets).all()
for tweet in tweets:
sid = SentimentIntensityAnalyzer()
scores = sid.polarity_scores(tweet.text)
sentiment = ""
if scores.get('compound') > 0.0:
sentiment = 'pos'
elif scores.get('compound') < 0.0:
sentiment = 'neg'
else:
sentiment = 'neu'
insert_sentiment_into_tweets(tweet.id_str, sentiment)
print("inserted")
print(sentiment)
with session_scope() as s:
retweets = s.query(Retweets).all()
for retweet in retweets:
sid = SentimentIntensityAnalyzer()
scores = sid.polarity_scores(retweet.text)
sentiment = ""
if scores.get('compound') > 0.0:
sentiment = 'pos'
elif scores.get('compound') < 0.0:
sentiment = 'neg'
else:
sentiment = 'neu'
insert_sentiment_into_retweets(retweet.id_str, sentiment)
print("inserted")
print(sentiment)
def get_location():
with session_scope() as s:
tweets = s.query(Tweets).all()
# tweets = get_all_tweets()
for tweet in tweets:
print(tweet.geo)
if tweet.geo is None:
location = get_user_location(tweet.user_id)
insert_location_into_tweets(tweet.id_str, location[0]['location'])
# adding location for retweets
with session_scope() as s:
retweets = s.query(Retweets).all()
for retweet in retweets:
if retweet.geo is None:
location = get_user_location(retweet.user_id)
insert_location_into_retweets(retweet.id_str, location[0]['location'])
def readCanaryRecommendations(userid):
with session_scope() as s:
since = datetime.now() - timedelta(hours=0.3)
# create query to get all recommendations served to user in the last 3 hours
q = s.query(CanaryRecommendation).filter(
CanaryRecommendation.served_at > since).filter(
CanaryRecommendation.user_id == userid)
recommendedMovies = []
# get a list of all movies recommended to the user and return
for row in q.all():
recommendedMovies += row.movie_ids
return recommendedMovies
def createRecommendation(reco: Recommendation):
with session_scope() as s:
s.add(reco)
def readCanaryRecommendationRatingAverage():
with session_scope() as s:
since = datetime.now() - timedelta(hours=0.3)
return s.query(func.avg(CanaryRecommendationRating.rating)).filter(
CanaryRecommendationRating.captured_at > since).scalar()
def createCanaryRecommendationRating(rating: CanaryRecommendationRating):
with session_scope() as s:
s.add(rating)
