cautious-broccoli/app.py at main · SinSuhas/cautious-broccoli · GitHub

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import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px

from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt

st.title("Sentiment Analysis of Tweets about US Airlines")
st.sidebar.title("Sentiment Analysis of Tweets about US Airlines")

st.markdown("This app is a streamlit dashboard to analyze the sentiments of tweets 😀")
st.sidebar.markdown("This app is a streamlit dashboard to analyze the sentiments of tweets 😀")

DATA_URL = ("/home/coder/Desktop/Project/Tweets.csv")

@st.cache_data(persist=True)
def load_data():
    data = pd.read_csv(DATA_URL)
    data['tweet_created']=pd.to_datetime(data['tweet_created'])
    return data

data = load_data()


st.sidebar.subheader("Show Random Tweet")
random_tweet = st.sidebar.radio('Sentiment Type',('positive','neutral','negative'))
st.sidebar.markdown(data.query('airline_sentiment == @random_tweet')[['text']].sample(n=1).iat[0,0])


st.sidebar.markdown("### Number of tweets by sentiment")
select =st.sidebar.selectbox('Visualization Type',['Histogram','Pie Chart'],key='1')


sentiment_count = data['airline_sentiment'].value_counts()
sentiment_count = pd.DataFrame({'Sentiment':sentiment_count.index,'Tweets':sentiment_count.values})


if not st.sidebar.checkbox("Hide",True):
    st.markdown("### Number of tweets by sentiment")
    if select == 'Histogram':
        fig = px.bar(sentiment_count,x='Sentiment',y='Tweets',color='Tweets', height=500)
        st.plotly_chart(fig)
    else:
        fig = px.pie(sentiment_count, values='Tweets',names='Sentiment')
        st.plotly_chart(fig)


st.sidebar.subheader("When and where are users tweeting from?")
hour = st.sidebar.slider("Hour of day",0,23)
modified_data = data[data['tweet_created'].dt.hour == hour]
if not st.sidebar.checkbox('Close the Map',True):
    st.markdown("### Tweet location based on the time of day")
    st.markdown("%i tweets between %i:00 and %i:00" % (len(modified_data), hour,(hour+1)%24))
    st.map(modified_data)
    if st.sidebar.checkbox("Show raw data",False):
        st.write(modified_data)

st.sidebar.subheader("Breakdown airline tweets by sentiment")
choice = st.sidebar.multiselect('Pick airlines',('US Airways', 'United','American','Southwest','Delta','Virgin America'),key='0')

if len(choice) > 0:
    choice_data = data[data.airline.isin(choice)]
    fig_choice = px.histogram(choice_data,x='airline', y='airline_sentiment', histfunc='count', color='airline_sentiment',
    facet_col='airline_sentiment', labels={'airline_sentiment':'tweets'})
    st.plotly_chart(fig_choice)

st.sidebar.header("Word Cloud")
word_sentiment = st.sidebar.radio("Display word cloud for what sentiment",('positive','neutral','negative'))
if not st.sidebar.checkbox("Hide",True,key='3'):
    st.subheader("Wordcloud  for %s sentiment" % (word_sentiment))
    df = data[data['airline_sentiment']==word_sentiment]
    words = ' '.join(df['text'])
    processed_word = ' '.join([word for word in words.split() if 'http' not in word and not word.startswith('@') and word != 'RT'])
    wordcloud = WordCloud(font_path='/home/coder/Desktop/Project/font.ttf',stopwords=STOPWORDS,background_color='weight').generate(processed_word)
    plt.imshow(wordcloud)
    plt.xticks([])
    plt.yticks([])
    st.pyplot()