def result():
text = request.form['comment']
result = text.upper()
stock_lex = pd.read_csv('C:/Users/baoannij/FlaskApp/stock_lex.csv')
stock_lex['sentiment'] = (stock_lex['Aff_Score'] +
stock_lex['Neg_Score']) / 2
stock_lex = dict(zip(stock_lex.Item, stock_lex.sentiment))
stock_lex = {k: v for k, v in stock_lex.items() if len(k.split(' ')) == 1}
stock_lex_scaled = {}
for k, v in stock_lex.items():
if v > 0:
stock_lex_scaled[k] = v / max(stock_lex.values()) * 4
else:
stock_lex_scaled[k] = v / min(stock_lex.values()) * -4
# Loughran and McDonald
positive = []
with open('C:/Users/baoannij/FlaskApp/pos.csv', 'r') as f:
reader = csv.reader(f)
for row in reader:
positive.append(row[0].strip())
negative = []
with open('C:/Users/baoannij/FlaskApp/neg.csv', 'r') as f:
reader = csv.reader(f)
for row in reader:
entry = row[0].strip().split(" ")
if len(entry) > 1:
negative.extend(entry)
else:
negative.append(entry[0])
sia = SentimentIntensityAnalyzer()
final_lex = {}
final_lex.update({word: 2.0 for word in positive})
final_lex.update({word: -2.0 for word in negative})
final_lex.update(stock_lex_scaled)
final_lex.update(sia.lexicon)
sia.lexicon = final_lex
score = sia.polarity_scores(result)
neg = score['neg']
neu = score['neu']
pos = score['pos']
compound = score['compound']
print(score)
print("{:-<40} {}".format(result, str(score)))
return render_template("analyze.html",
result=result,
neg=neg,
neu=neu,
pos=pos,
compound=compound)
for row in reader:
positive.append(row[0].strip())
negative = []
with open('C:/Users/baoannij/Downloads/neg.csv', 'r') as f:
reader = csv.reader(f)
for row in reader:
entry = row[0].strip().split(" ")
if len(entry) > 1:
negative.extend(entry)
else:
negative.append(entry[0])
sia = SentimentIntensityAnalyzer()
final_lex = {}
final_lex.update({word: 2.0 for word in positive})
final_lex.update({word: -2.0 for word in negative})
final_lex.update(stock_lex_scaled)
final_lex.update(sia.lexicon)
sia.lexicon = final_lex
def sentiment_analyzer_scores(sentence):
score = sia.polarity_scores(sentence)
print(str(score))
print("{:-<40} {}".format(sentence, str(score)))
sentiment_analyzer_scores("today is a great day")
