app.py

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

### Adding a Title and Sidebar:
st.title("Sentimental Analysis of Tweets about US Airlines")
st.sidebar.title("Sentimental Analysis of Tweets about US Airlines")

### Adding Description in main page and sidebar:
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. 🐦")

### Loading the data:
#Tweets.csv
DATA_URL = ("Tweets.csv")
#"C:\Users\akank\PycharmProjects\AirlineSentimentAnalysisDashboard\Tweets2.csv"

@st.cache(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.write(data)

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

### Data Visualization:
### Showing the number of Tweets by Sentiment using a Histogram or Pie Chart based on user's preference:
st.sidebar.markdown("### Number of Tweets by Sentiment")
select = st.sidebar.selectbox('Visulaization 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)

### Showing the when and where these tweets were made from:
st.sidebar.subheader("When and Where are the 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", True, key='1'):
    st.markdown("### Tweets locations 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)

### Displaying the number of positve, negative or neutral airlines by airlines:
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'}, height=600,
                              width=800)
    st.plotly_chart(fig_choice)

st.sidebar.header("Word Cloud")
word_sentiment = st.sidebar.radio('Display word cloud for which 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()
    st.set_option('deprecation.showPyplotGlobalUse', False)