|
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.set_option('deprecation.showPyplotGlobalUse', False)
|
|
|
|
DATA_ = pd.read_csv("Tweets.csv")
|
|
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 sentiment of Tweets")
|
|
st.sidebar.markdown("This application is a streamlit dashboard to analyze the sentiment of Tweets")
|
|
|
|
|
|
def run():
|
|
|
|
@st.cache(persist=True)
|
|
def load_data():
|
|
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', ('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'])
|
|
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", True, key='1'):
|
|
st.markdown("### Tweets locations based on the time of date")
|
|
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 airline', ('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 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()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
run()
|
|
|
|
|
