def SA_text(text_content, service_account):
client = language_v1.LanguageServiceClient.from_service_account_json(
service_account)
type_ = language_v1.Document.Type.PLAIN_TEXT
document = {"content": text_content, "type_": type_}
encoding_type = language_v1.EncodingType.UTF8
try:
response = client.analyze_sentiment(request={
'document': document,
'encoding_type': encoding_type
})
except exceptions.InvalidArgument as e:
# if there is an invalid exception
print('ERROR ANALYSING TEXT USING GOOGLE ANALYSIS:')
print(e.message)
return None
# Score is the overall emotional learning of the text
# Magnitude indicates the overall strength of the emotion.
score = response.document_sentiment.score
magnitude = response.document_sentiment.magnitude
sentimemt = Sentiment.Sentiment(score, magnitude)
print('SA RESULTS TEXT: {}'.format(text_content))
print(sentimemt)
return sentimemt
def analyse_sentiment(data, tweets):
analyser = st.Sentiment(tweets)
vader_sentiments, vader_score = analyser.check_sentiment_vader()
vader_data = append_to_data_frame(data, 'Nltk_Sentiment_Score',
vader_score)
vader_data = append_to_data_frame(vader_data, 'Nltk_Sentiment',
vader_sentiments)
return vader_data
def main():
review = Review("", "", "", "", "", "", "", "", "",
"") # this will call your constructor
# get 50 results from databases
reviews = review.get_reviews("50")
for a_review in reviews:
#construction of sentiment table
blob = TextBlob(a_review.text, analyzer=NaiveBayesAnalyzer())
text_sentiment = blob.sentiment
text_sentiment = text_sentiment[
SENTIMENT_TYPE] #text_sentiment will either be pos (for positive) or neg (for negative)
#here is where we create a Sentiment object
sentiment = Sentiment(a_review.review_id, a_review.business_id,
text_sentiment)
sentiment.insert(
) #this will insert information into the sentiment table
#construction of review_stats table
review_stats = Review_stats("", "", "", "", "")
review_stats.insert(
) #insert positive and negative reviews' information to review_stats table
#construction of common_phrases table
business = Business("", "", "", "", "", "", "", "", "", "", "")
business_ids = business.get_all_business_ids(
) #acquire all business_ids from sentiment table
Reviews = list()
words = list()
list_of_words = list()
word_dictionary = dict()
for business_id in business_ids:
review = Review("", "", "", "", "", "", business_id.business_id, "",
"", "")
Reviews = review.get_reviews_by_business_id(
) # get all reviews by business_id
insert_words(business_id.business_id, Reviews,
word_dictionary) # insert data into common_phrases table
def __init__(self, subreddit, sentimentInterval, dataSet):
self.subreddit, self.sentimentInterval = subreddit, sentimentInterval
self.subInstance = praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent="testscript by u/spaceballcookie").subreddit(subreddit)
self.df = pd.DataFrame(columns=['postID', 'ticker', 'sentiment', 'magnitude','subreddit', 'permalink', 'date', 'body'])
self.sentiment = Sentiment()
self.dataSet = dataSet
from Sentiment import * import sys f = sys.argv[1] S = Sentiment(f)
