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import pandas as pd
from nltk.corpus import stopwords
from textblob import Word, TextBlob
stop_words=stopwords.words('english')
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
def replace_non_alphanumeric(text):
result = ""
for char in text:
if char.isalnum() or char.isspace():
result += char
return result
def preprocess_texts(text):
processed_text = replace_non_alphanumeric(text)
processed_text = " ".join(word for word in processed_text.split() if word not in stop_words)
processed_text = " ".join(Word(word).lemmatize() for word in processed_text.split())
return processed_text
def get_polarity_subjectivity(preprocess_text):
processed_text=preprocess_texts(preprocess_text)
polarity = TextBlob(processed_text).sentiment[0]
subjectivity = TextBlob(processed_text).sentiment[1]
return polarity, subjectivity
def sentiment_analysis(text):
processed_text=preprocess_texts(text)
sia=SentimentIntensityAnalyzer()
sentiment=sia.polarity_scores(text)
return sentiment
# use microphone input in the future.
text=input()
dict_sentiment = (sentiment_analysis(text))
score = dict_sentiment['compound']
if score < -.3:
print("Alert")
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