def on_data(self, data):
tweetJSON = json.loads(data)
lineJSON = {}
try:
sentiment_line = tweetJSON['text']
lineJSON['text'] = tweetJSON['text']
lineJSON['followers_count'] = tweetJSON['user']['followers_count']
lineJSON['timestamp_ms'] = tweetJSON['timestamp_ms']
lineJSON['text'] = tweetJSON['text']
except KeyError:
pass
sentiment_line = ""
sentiment_score = sentiment.analyse(sentiment_line)
lineJSON['sentiment'] = sentiment_score
self.pp.pprint(lineJSON)
now = time.localtime(time.time())[7]
if self.today != now:
self.moveToZip(self.today)
self.today = now
self.save_as_JSON(lineJSON, self.today)
if sentiment_score != 0:
self.no_of_tweets += 1
self.total_sentiment_score += sentiment_score
try:
print("AVG sentiment: " +
str(self.total_sentiment_score / self.no_of_tweets))
except ZeroDivisionError:
pass
return True
def eval(file_name, choice):
text = list()
if choice == 1:
text.append(recognize(file_name))
else:
with open(file_name, "rt") as f:
lines = f.readlines()
print(lines)
for line in lines:
text.append(line)
nb_count = 0
svm_count = 0
print(text)
total = len(text)
for i in text:
if analyse(i) <= 0.1:
if predict_nb(i) == 'suicidal':
nb_count = nb_count + 1
if predict_svm(i) == 'suicidal':
svm_count = svm_count + 1
avg_count = (nb_count + svm_count) / 2
if choice == 1:
if avg_count == 1:
return 'Potentially suicidal content.'
else:
return 'No suicidal content found.'
else:
print(avg_count)
print(total)
percentage = (avg_count / total) * 100
return percentage
if '--chunk' in flags:
print('Running Tag and Chunker')
news_df['chunked_text'] = chunker.tag_and_chunk(news_df['full_text'])
print('Dataset dumped to: news_chunked.csv')
news_df.to_csv('news_chunked.csv', index=False, encoding='utf-8')
if '--stanford' in flags:
print('Running StanfordParser')
news_df['stanford_text'] = stanford.parse(news_df['full_text'])
print('Dataset dumped to: news_stanford.csv')
news_df.to_csv('news_stanford.csv', index=False, encoding='utf-8')
if '--dep_parse' in flags:
print('Running Dep Parser')
news_df['deps_text'] = dependency_parser.parse(news_df['full_text'])
print('Dataset dumped to: news_dependency.csv')
news_df.to_csv('news_dependency.csv', index=False, encoding='utf-8')
if '--named_ents' in flags:
print('Running Named Entities Extracter')
named_ents = named_entities.parse(news_df['full_text'])
print('Dataset dumped to: news_named_ents.csv')
named_ents.to_csv('news_named_ents.csv', index=False, encoding='utf-8')
if '--sentiment' in flags:
print('Running Sentiment Analysis')
sentiments = sentiment.analyse(news_df['full_text'],
list(news_df['news_category']))
print('Dataset dumped to: news_sentiment.csv')
sentiments.to_csv('news_sentiment.csv', index=False, encoding='utf-8')
import sentiment
import json
# sentiment.analyse expects a list of (review string, rating) tuples.
# This example uses academic dataset https://www.yelp.com/academic_dataset
# It's around 200 mb so I won't be uploading it to github.
labMTPath = "happiness.txt"
yelp = "..\Yelp\yelp_phoenix_academic_dataset\yelp_academic_dataset_review.json"
def loadYelpData(path):
reviews = []
with open(path, "r") as f:
for line in f:
dict = json.loads(line)
reviews += [(dict["text"], float(dict["stars"]))]
return reviews
sentiment.analyse(loadYelpData(yelp), labMTPath, "yelpSentiment.pkl")
sentiment.visualise("yelpSentiment.pkl")
# sentiment.analyse expects a list of (review string, rating) tuples.
# This example uses academic dataset https://www.yelp.com/academic_dataset
# It's around 200 mb so I won't be uploading it to github.
labMTPath = "happiness.txt"
grPath = "../../data/goodreads.20130510.txt"
def loadYelpData(path):
reviews = []
with open(path, "r") as f:
for line in f:
dict = json.loads(line)
reviews += [(dict["text"], float(dict["stars"]))]
return reviews
def loadGoodreadsData(path):
reviews = []
input_ = open(path, "rb")
data = pickle.load(input_)
for i in range(len(data["stars"])):
reviews += [(data["reviews"][i], data["stars"][i])]
return reviews
data = loadGoodreadsData(grPath)
#print data
sentiment.analyse(data, labMTPath, "goodreadsSentiment.pkl")
sentiment.visualise('goodreadsSentiment.pkl')