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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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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_data(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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