def findFrequencies(self, tweets):
model = EmotionPredictor(classification='ekman',
setting='mc',
use_unison_model=True)
self.__predictions = model.predict_classes(tweets)
i = 0
for index, row in self.__predictions.iterrows():
self.__emotionFrequency[row['Emotion']] += 1
# print (noOfTweets) model = EmotionPredictor(classification='plutchik', setting='mc') # tweets = [ # "Watching the sopranos again from start to finish!", # "Finding out i have to go to the dentist tomorrow", # "I want to go outside and chalk but I have no chalk", # "Stock market hit all time low on friday", # "Stock market hit all time high on friday", # "I believe you", # "My mom wasn't mad", # "Do people have no Respect for themselves or you know others peoples homes", # "www.google.com I am happy", # ] result = model.predict_classes(tweets) probabilities=model.predict_probabilities(tweets) print (result) print (probabilities) print (probabilities.to_dict[0]) anger = probabilities["Anger"].values.tolist() joy = probabilities["Joy"].values.tolist() fear = probabilities["Fear"].values.tolist() surprise = probabilities["Surprise"].values.tolist() disgust = probabilities["Disgust"].values.tolist() trust = probabilities["Trust"].values.tolist() anticipation = probabilities["Anticipation"].values.tolist() sadness = probabilities["Sadness"].values.tolist() text = result["Tweet"].values.tolist()
# pd.options.display.max_columns = 7 # maximal number of columns
# Predictor for Ekman's emotions in multiclass setting.
model = EmotionPredictor(classification='ekman',
setting='mc',
use_unison_model=True)
tweets = []
with open(
'/home/didier/ASONAMW/PA/Sentiment/Billboard_Music_Awards_2016_Sentiment.txt',
'r') as csvfile:
plots = csv.reader(csvfile, delimiter=';')
for row in plots:
tweets.append(row[1])
predictions = model.predict_classes(tweets)
predictions.to_csv(
"/home/didier/ASONAMW/PA/Sentiment/Billboard_Music_Awards_2016_Emotions.txt",
sep=';',
encoding='utf-8')
tweets = []
with open(
'/home/didier/ASONAMW/PA/Sentiment/Billboard_Music_Awards_2017_Sentiment.txt',
'r') as csvfile:
plots = csv.reader(csvfile, delimiter=';')
for row in plots:
tweets.append(row[1])
predictions = model.predict_classes(tweets)
predictions.to_csv(
with open(file, "r+") as rf, tempfile:
reader = csv.reader(rf, delimiter=',')
writer = csv.writer(tempfile, delimiter=',')
for tweetRow in reader:
# emotions
# print (tweetRow)
if (tweetRow[0] == "id"):
continue
words = []
emotions = {}
if (tweetRow[17] == "-"):
word = tweetRow[13]
words.append(word)
# print (words)
result = model.predict_classes(words)
probabilities = model.predict_probabilities(words)
anger = probabilities["Anger"].values.tolist()
joy = probabilities["Joy"].values.tolist()
fear = probabilities["Fear"].values.tolist()
surprise = probabilities["Surprise"].values.tolist()
disgust = probabilities["Disgust"].values.tolist()
trust = probabilities["Trust"].values.tolist()
anticipation = probabilities["Anticipation"].values.tolist()
sadness = probabilities["Sadness"].values.tolist()
emotions["Anger"] = anger[0]
emotions["joy"] = joy[0]
emotions["fear"] = fear[0]
emotions["surprise"] = surprise[0]
from emotion_predictor import EmotionPredictor
model = EmotionPredictor(classification='plutchik',
setting='mc',
use_unison_model=True)
print("************")
predictions = model.predict_classes(["I like to play cricket"])
print(predictions.iloc[0]['Tweet'], '\t', predictions.iloc[0]['Emotion'])
#path_to_json = 'C:/Users/Αναστασία/Desktop/json/'
#for file_name in [file for file in os.listdir(path_to_json) if file.endswith('.json')]:
# with open(path_to_json + file_name) as json_file:
# data=json.load(json_file)
# read list inside dict
#_list = data['Reviews']
# read listvalue and load dict
#for v in _list:
# reviews.append(v['Content'])
#predictions = model.predict_classes(reviews)
#probabilities = model.predict_probabilities(reviews)
#embeddings = model.embed(reviews)
#forOneJsonFile
with open('C:/Users/Αναστασία/Desktop/json/72572.json', 'r') as f:
data = json.load(f)
# read list inside dict
_list = data['Reviews']
# read listvalue and load dict
for v in _list:
reviews.append(v['Content'])
predictions = model.predict_classes(reviews)
print(predictions, '\n')
probabilities = model.predict_probabilities(reviews)
print(probabilities, '\n')
embeddings = model.embed(reviews)
print(embeddings, '\n')
#pathOfJsonFiles
path_to_json = 'json/'
for file_name in [file for file in os.listdir(path_to_json) if file.endswith('.json')]:
with open(path_to_json + file_name) as json_file:
data= []
data=json.load(json_file)
# read list inside dict
_list = data['Reviews']
# read listvalue and load dict
for v in _list:
if ('Title' in v):
fullContent = v['Title']
else:
fullContent = ''
fullContent = fullContent + ' ' + v['Content']
reviews.append(fullContent)
ratings.append(v['Ratings'])
authors.append(v['Author'])
hotels.append(data['HotelInfo']['HotelID'])
predictions = model.predict_classes(reviews, ratings, authors, hotels)
print(predictions, '\n')
probabilities = model.predict_probabilities(reviews, ratings, authors, hotels)
print(probabilities, '\n')
embeddings = model.embed(reviews, ratings, authors, hotels)
print(embeddings, '\n')
tweet = emoji.demojize(tweet)
tweet = tweet.replace(":"," ")
tweet = ' '.join(tweet.split())
return tweet
def removeContent(df:str, colname:str, *args):
sens ='df[{}]'.format("|".join(["df.{}.str.contains('"'{}'"')".format(colname,x) for x in list(args)]))
df_other_list1 = list(eval(sens)[colname])
df_other_list2 = list(df[colname])
ret = list(set(df_other_list2) ^ set(df_other_list1))
result = df[df[colname].isin(ret)]
return result
tweets_table = pd.read_csv("clean#excitement.csv",usecols=["id","created_at","new_text"])
model = EmotionPredictor(classification='ekman', setting='mc', use_unison_model=True)
predictions = model.predict_classes([process_emoji(tw) for tw in tweets_table.new_text.tolist()])
#print(predictions, '\n')
tweets_table["emotion"]=predictions["Emotion"].tolist()
result=removeContent(tweets_table.astype(str),"emotion", 'Disgust', 'Fear','Sadness')
#print(result)
print(result.shape[0])
result=result.drop(['emotion'],axis=1)
result.to_csv("cs_excitement.csv", mode='a',encoding="utf-8",index=None)