from altair.vegalite.v4.schema.core import Header 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()