def __getEmotions__(self, doc):
try:
emos = te.get_emotion(doc)
mostCommonEmos = Counter(emos).most_common(2)
return [e for e, prob in mostCommonEmos]
except:
return []
def getEmotion(text):
translator = Translator()
result = translator.translate(text, dest='en')
print("Tranlsated text:", result.text)
print("Text emotion:", te.get_emotion(result.text))
def on_status(self, status):
if (time.time() - self.start_time) < self.time_limit:
print(status.text)
print(
"***************************Emotions detected******************************************"
)
data_dict = get_emotion(status.text)
data_dict = {k: float(v) for k, v in data_dict.items()}
data = pd.DataFrame([data_dict])
chart.add_rows(data)
print(data_dict)
return True
else:
return False
def detection():
#mengecek apakah method yang di pakai di form index.html yaitu POST atau bukan
if request.method == 'POST':
# mengambil data inputan komentar dan di simpan dalam varibel komentar
komentar = request.form['komentar']
# textblob sentiment polarity akan menghasilkan angka 1 atau -1
# bila 1 komentar tersebut positif, bila -1 komentar tersebut negatif
feedback_polarity = TextBlob(komentar).sentiment.polarity
if feedback_polarity > 0:
result = 'positif'
else:
result = 'negatif'
#inisialisasi varibael data yang isinya list
data = list()
# Call to the function
# variabel dict_emotion dan percent_emotion menyimpan hasil dari analisis kalimat
dict_emotion = te.get_emotion(komentar)
percent_emotion = te.get_percent_emotion(komentar)
# perulangan ini untuk mendapatkan value dari dictionary {"Happy": 0.5, "Angry": 0, "Surprise": 3.5, "Sad": 0, "Fear": 0}
for key, value in dict_emotion.items():
#disini akan masuk ke list data value dari happy dan surprise
data.append(value)
#inisialisasi emotions untuk dimasukkan ke dalam grafik
emotions = ['Happy', 'Angry', 'Surprise', 'Sad', 'Fear']
#konfigurasi dari grafik yang akan dibuat gambar
fig = plt.figure(figsize=(10, 7))
plt.pie(data, labels=emotions, normalize=False)
#membuat hasil menjadi gambar
pie_chart = pygal.Pie()
pie_chart.title = 'Pie chart'
pie_chart.add('Happy', percent_emotion['Happy'])
pie_chart.add('Angry', percent_emotion['Angry'])
pie_chart.add('Surprise', percent_emotion['Surprise'])
pie_chart.add('Sad', percent_emotion['Sad'])
pie_chart.add('Fear', percent_emotion['Fear'])
pie_chart = pie_chart.render_data_uri()
return render_template("index.html",
hasil=result,
komen=komentar,
chart=pie_chart,
percent_emotion=percent_emotion)
def transform(text):
emotion_ratings = te.get_emotion(text)
emotion_ratings
significant_emotion = max(emotion_ratings, key=emotion_ratings.get)
kw_extractor = yake.KeywordExtractor()
language = 'en'
max_ngram_size = 3
deduplication_threshhold = 0.9
numOfKeywords = 10
custom_kw_extractor = yake.KeywordExtractor(
lan=language,
n=max_ngram_size,
dedupLim=deduplication_threshhold,
top=numOfKeywords,
features=None)
keywords = custom_kw_extractor.extract_keywords(text)
keywords.reverse()
top_keywords = keywords[:5]
significant_emotion
emotion_colors = {
'Happy': 'Yellow',
'Angry': 'Red',
'Surprise': 'Green',
'Sad': 'Blue',
'Fear': 'Dark'
}
significant_color = emotion_colors[significant_emotion]
top_keywords_wo_scores = [x[0] for x in top_keywords]
top_keywords_wo_scores
final_string = '' + significant_emotion + ' ' + significant_color
for i in top_keywords_wo_scores:
final_string += ' ' + i
return final_string
