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from altair.vegalite.v4.schema.core import Header
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import streamlit as st
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import pandas as pd
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import numpy as np
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import plotly.express as px
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from wordcloud import WordCloud, STOPWORDS
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import matplotlib.pyplot as plt
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st.set_option('deprecation.showPyplotGlobalUse', False)
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DATA_ = pd.read_csv("Tweets.csv")
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st.title("Sentiment Analysis of Tweets about US Airlines")
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st.sidebar.title("Sentiment Analysis of Tweets about US Airlines")
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st.markdown("This application is a streamlit dashboard to analyze the sentiment of Tweets")
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st.sidebar.markdown("This application is a streamlit dashboard to analyze the sentiment of Tweets")
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def run():
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@st.cache(persist=True)
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def load_data():
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DATA_['tweet_created'] = pd.to_datetime(DATA_['tweet_created'])
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return DATA_
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data = load_data()
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st.sidebar.subheader("Show random tweet")
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random_tweet = st.sidebar.radio('Sentiment', ('positive', 'neutral', 'negative'))
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st.sidebar.markdown(data.query('airline_sentiment == @random_tweet')[["text"]].sample(n=1).iat[0,0])
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st.sidebar.markdown("### Number of tweets by sentiment")
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select = st.sidebar.selectbox('Visualization type', ['Histogram', 'Pie chart'])
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sentiment_count = data['airline_sentiment'].value_counts()
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sentiment_count = pd.DataFrame({'Sentiment':sentiment_count.index, 'Tweets':sentiment_count.values})
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if not st.sidebar.checkbox("Hide", True):
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st.markdown("### Number of tweets by sentiment")
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if select == "Histogram":
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fig = px.bar(sentiment_count, x='Sentiment', y='Tweets', color='Tweets', height=500)
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st.plotly_chart(fig)
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else:
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fig = px.pie(sentiment_count, values='Tweets', names='Sentiment')
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st.plotly_chart(fig)
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st.sidebar.subheader("When and Where are users tweeting from?")
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hour = st.sidebar.slider("Hour of day", 0,23)
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modified_data = data[data['tweet_created'].dt.hour == hour]
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if not st.sidebar.checkbox("Close", True, key='1'):
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st.markdown("### Tweets locations based on the time of date")
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st.markdown("%i tweets between %i:00 and %i:00" % (len(modified_data), hour, (hour+1)%24))
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st.map(modified_data)
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if st.sidebar.checkbox("Show Raw Data", False):
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st.write(modified_data)
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st.sidebar.subheader("Breakdown airline tweets by sentiment")
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choice = st.sidebar.multiselect('Pick airline', ('US Airways', 'United', 'American', 'Southwest', 'Delta', 'Virgin America'), key='0')
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if len(choice) > 0:
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choice_data = data[data.airline.isin(choice)]
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fig_choice = px.histogram(choice_data, x='airline',
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y='airline_sentiment',
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histfunc = 'count', color = 'airline_sentiment',
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facet_col='airline_sentiment',
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labels={'airline_sentiment':'tweets'}, height=600, width=800)
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st.plotly_chart(fig_choice)
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st.sidebar.header("Word Cloud")
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word_sentiment = st.sidebar.radio('Display word cloud for what sentiment?',('positive', 'neutral','negative'))
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if not st.sidebar.checkbox("Close", True, key='3'):
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st.header('Word cloud for %s sentiment' % (word_sentiment))
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df = data[data['airline_sentiment']==word_sentiment]
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words = ' '.join(df['text'])
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processed_words = ' '.join([word for word in words.split() if 'http' not in word and not word.startswith('@') and word !='RT'])
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wordcloud = WordCloud(stopwords=STOPWORDS,
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background_color='white', height=640, width=800).generate(processed_words)
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plt.imshow(wordcloud)
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plt.xticks([])
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plt.yticks([])
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st.pyplot()
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if __name__ == '__main__':
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run()
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