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import pandas as pd
import numpy as np
import streamlit as st
import altair as alt
from textblob import TextBlob
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
# Functions
def convert_to_df(sentiment):
sentiment_dict = {"polarity":sentiment.polarity,"subjectivity":sentiment.subjectivity}
sentiment_df = pd.DataFrame(sentiment_dict.items(),columns=["metric","value"])
return sentiment_df
def analyze_token_sentiment(docx):
analyzer = SentimentIntensityAnalyzer()
pos_list = []
neg_list = []
neu_list = []
for i in docx.split():
res = analyzer.polarity_scores(i)["compound"]
if res >= 0.1:
pos_list.append(i)
pos_list.append(res)
elif res <= -0.1:
neg_list.append(i)
neg_list.append(res)
else:
neu_list.append(i)
result = {"positives":pos_list, "negatives":neg_list, "neutral":neu_list}
return result
def main():
st.title("Sentiment Analysis NLP App using Streamlit")
st.subheader("Reformation Team Project")
menu = ["Home","About"]
choice = st.sidebar.selectbox("Menu",menu)
if choice == "Home":
st.subheader("Home")
with st.form(key="nlpForm"):
raw_text = st.text_area("Enter Text Here")
submit_button = st.form_submit_button(label="Analyze")
#layout
col1, col2 = st.columns(2)
if submit_button:
with col1:
st.info("Results")
sentiment = TextBlob(raw_text).sentiment
st.write(sentiment)
#Emoji
if sentiment.polarity > 0:
st.markdown("Sentiment:: Positive :smiley: ")
elif sentiment.polarity <0:
st.markdown("Sentiment:: Negative :angry: ")
else:
st.markdown("Sentiment:: Neutral :😐: ")
# Dataframe
result_df = convert_to_df(sentiment)
st.dataframe(result_df)
# Visualization
c = alt.Chart(result_df).mark_bar().encode(
x="metric",
y="value",
colour="metric")
st.altair_chart(c,use_container_width=True)
with col2:
st.info("Token Sentiment")
token_sentiments = analyze_token_sentiment(raw_text)
st.write(token_sentiments)
else:
st.subheader("About")
if __name__ == "__main__":
main()
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