Dashboard.py

# -*- coding: utf-8 -*-
"""
Created on Mon Jun 15 00:12:45 2020

@author: Sayanti Dutta
"""

import streamlit as st
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
from wordcloud import WordCloud,STOPWORDS

st.title("Sentiment Analysis of Tweets about US Airlines")
st.sidebar.title("Sentiment Analysis of Tweets about US Airlines")
st.markdown("This application is a Streamlit dashboard to analyze the sentiments of tweets🐦 ")
st.sidebar.markdown("This application is a Streamlit dashboard to analyze the sentiments of tweets🐦 ")

@st.cache(persist="True")
def load_data():
    data=pd.read_csv("G:/codes/Python codes/Dashboard/Tweets.csv")
    data['tweet_created']=pd.to_datetime(data['tweet_created'])
    return data
data=load_data()

st.sidebar.subheader("Show random Tweets")
random_tweet=st.sidebar.radio('Sentiment',('positive','negative','neutral'))
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 people are 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" , True, key='1'):
    st.markdown("### Tweets location based on the time of the day")
    st.markdown("%i tweets between %i:00 and %i:00 hour" % (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','Virgin America','Delta','American','South West'))

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','airline':'Airline',},height=600,width=800)
   st.plotly_chart(fig_choice)
   
st.sidebar.header("Wordcloud")
word_sentiment=st.sidebar.radio('Display Wordcloud for what sentiment?',('positive','neutral','negative'))
if not st.sidebar.checkbox("Close", True, key='3'):
    st.header("Word cloud for %s sentiment" % (word_sentiment))
    df=data[data['airline_sentiment']==word_sentiment]
    words=''.join(df['text'])
    processed_words=''.join([word for word in words.split() if 'http' not in word and not word.startswith('@') and word!='RT'])
    wordcloud=WordCloud(stopwords=STOPWORDS,background_color='white',height=640,width=800).generate(processed_words)
    plt.imshow(wordcloud)
    plt.xticks([])
    plt.yticks([])
    st.pyplot()