Streamlit Dashboard for Twitter Sentiment Analysis using Python.

By Madhav Sharma

In this project, we will be building our interactive Web-app data dashboard using streamlit library in Python. We will be doing sentiment analysis of Twitter US Airline Data.

Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. In just a few minutes you can build and deploy powerful data apps

 

We are going to focus on three objectives:

 

1. Build interactive data dashboards with Streamlit and Python.

2. Use pandas for data manipulation in data science workflows.

3. Create interactive plots with Plotly Python.

 

So, our project will be divided into following task

 

Task 1: Install streamlit library

(i)Make sure that you have Python 3.6 or greater installed.

(ii)Install Streamlit using PIP:

pip install streamlit

 

 

(iv)Run the hello world app:

streamlit hello

 

 

 

(v)In the next few seconds the sample app will open in a new tab in your default browser.

 

Task 2: Import packages and Libraries

 

import streamlit as st

import pandas as pd

import numpy as np

import plotly.express as px

from plotly.subplots import make_subplots

import plotly.graph_objects as go from wordcloud

import WordCloud, STOPWORDS import matplotlib.pyplot as plt

 

Task 3: Load the Twitter US Airline Sentiment Data

 

DATA_URL = (
    "Tweets.csv"
)

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

@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()

 

Task 4: Display Tweets in the Sidebar

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])

 

Task 5: Plot Interactive Bar Plots and Pie Charts

st.sidebar.markdown("### Number of tweets by sentiment")
select = st.sidebar.selectbox('Visualization type', ['Bar plot', '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 == 'Bar plot':
        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)

 

Task 6: Plotting Location Data on an Interactive Map

st.sidebar.subheader("When and where are users tweeting from?")
hour = st.sidebar.slider("Hour to look at", 0, 23)
modified_data = data[data['tweet_created'].dt.hour == hour]
if not st.sidebar.checkbox("Close", True, key='1'):
    st.markdown("### Tweet locations based on 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)

 

Task 7: Plot Number of Tweets by Sentiment for Each Airline

st.sidebar.subheader("Total number of tweets for each airline")
each_airline = st.sidebar.selectbox('Visualization type', ['Bar plot', 'Pie chart'], key='2')
airline_sentiment_count = data.groupby('airline')['airline_sentiment'].count().sort_values(ascending=False)
airline_sentiment_count = pd.DataFrame({'Airline':airline_sentiment_count.index, 'Tweets':airline_sentiment_count.values.flatten()})
if not st.sidebar.checkbox("Close", True, key='2'):
    if each_airline == 'Bar plot':
        st.subheader("Total number of tweets for each airline")
        fig_1 = px.bar(airline_sentiment_count, x='Airline', y='Tweets', color='Tweets', height=500)
        st.plotly_chart(fig_1)
    if each_airline == 'Pie chart':
        st.subheader("Total number of tweets for each airline")
        fig_2 = px.pie(airline_sentiment_count, values='Tweets', names='Airline')
        st.plotly_chart(fig_2)

Task 8: Ploting Sentiment

@st.cache(persist=True)
def plot_sentiment(airline):
    df = data[data['airline']==airline]
    count = df['airline_sentiment'].value_counts()
    count = pd.DataFrame({'Sentiment':count.index, 'Tweets':count.values.flatten()})
    return count


st.sidebar.subheader("Breakdown airline by sentiment")
choice = st.sidebar.multiselect('Pick airlines', ('US Airways','United','American','Southwest','Delta','Virgin America'))
if len(choice) > 0:
    st.subheader("Breakdown airline by sentiment")
    breakdown_type = st.sidebar.selectbox('Visualization type', ['Pie chart', 'Bar plot', ], key='3')
    fig_3 = make_subplots(rows=1, cols=len(choice), subplot_titles=choice)
    if breakdown_type == 'Bar plot':
        for i in range(1):
            for j in range(len(choice)):
                fig_3.add_trace(
                    go.Bar(x=plot_sentiment(choice[j]).Sentiment, y=plot_sentiment(choice[j]).Tweets, showlegend=False),
                    row=i+1, col=j+1
                )
        fig_3.update_layout(height=600, width=800)
        st.plotly_chart(fig_3)
    else:
        fig_3 = make_subplots(rows=1, cols=len(choice), specs=[[{'type':'domain'}]*len(choice)], subplot_titles=choice)
        for i in range(1):
            for j in range(len(choice)):
                fig_3.add_trace(
                    go.Pie(labels=plot_sentiment(choice[j]).Sentiment, values=plot_sentiment(choice[j]).Tweets, showlegend=True),
                    i+1, j+1
                )
        fig_3.update_layout(height=600, width=800)
        st.plotly_chart(fig_3)
st.sidebar.subheader("Breakdown airline 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_0 = 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_0)

 

 

Task 9: Word Cloud for Positive, Neutral, and Negative Tweets

st.sidebar.header("Word Cloud")
word_sentiment = st.sidebar.radio('Display word cloud for what sentiment?', ('positive', 'neutral', 'negative'))
if not st.sidebar.checkbox("Close", True, key='3'):
    st.subheader('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', width=800, height=640).generate(processed_words)
    plt.imshow(wordcloud)
    plt.xticks([])
    plt.yticks([])
    st.pyplot()

 

Congratulations! We made to the final step. 

Final Step:

Now open the terminal and run this command.

streamlit run app.py

And you will see our interactive dashboard app will open in a new tab in your default browser.