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from textblob import TextBlob
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from transformers import pipeline
import streamlit as st
def translate_text(text):
blob = TextBlob(text)
return str(blob.translate(from_lang="pt", to="en"))
def sentiment_classification(sentence):
sid_obj = SentimentIntensityAnalyzer()
sentiment_dict = sid_obj.polarity_scores(sentence)
negative = sentiment_dict['neg']
neutral = sentiment_dict['neu']
positive = sentiment_dict['pos']
compound = sentiment_dict['compound']
if sentiment_dict['compound'] >= 0.05 :
overall_sentiment = "Positive"
elif sentiment_dict['compound'] <= - 0.05 :
overall_sentiment = "Negative"
else :
overall_sentiment = "Neutral"
return overall_sentiment, sentiment_dict['compound']
def theme_classification(text):
labels = ["Industrial Goods",
"Communications",
"Cyclic Consumption",
"Non-cyclical Consumption",
"Financial",
"Basic Materials",
#"Others",
"Oil, Gas and Biofuels",
"Health",
#"Initial Sector",
"Information Technology",
"Public utility"]
template = "The economic sector of this set of words is {}."
classifier = pipeline("zero-shot-classification", model="joeddav/xlm-roberta-large-xnli")
results = classifier(text, labels, hypothesis_template=template)
index = results["scores"].index(max(results["scores"]))
return results["labels"][index]
text = st.text_area("Coloque seu texto sobre mercado financeiro em português!")
if text:
text_en = translate_text(text)
st.write("Translation: {}".format(text_en))
sentiment = sentiment_classification(text_en)
st.write("Sentiment: {} - {}".format(sentiment[0], sentiment[1]))
theme = theme_classification(text_en)
st.write("Theme: {}".format(theme)) |