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 import folium import plotly.express as px import seaborn as sns import json import os from streamlit_folium import folium_static st.set_option('deprecation.showPyplotGlobalUse', False) DATA_ = pd.read_csv("combined-csv-files.csv") st.title("Sentiment Analysis of Tweets") st.sidebar.title("Sentiment Analysis of Tweets") 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_['Datetime']) 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('sentiment_flair == @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['sentiment_flair'].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 language tweets by sentiment") choice = st.sidebar.multiselect('Pick language', ('en', 'hi'), key='0') if len(choice) > 0: choice_data = data[data.language.isin(choice)] fig_choice = px.histogram(choice_data, x='language', y='sentiment_flair', histfunc = 'count', color = 'sentiment_flair', facet_col='sentiment_flair', labels={'sentiment_flair':'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['sentiment_flair']==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() #################################### choropleth map ############################################################# with open('india_state.json') as file: geojsonData = json.load(file) for i in geojsonData['features']: i['id'] = i['properties']['NAME_1'] map_choropleth_high_public = folium.Map(location = [20.5937,78.9629], zoom_start = 4) df1 = data df1 = df1[df1['location'].notna()] def get_state(x): states = ["Andaman and Nicobar Islands","Andhra Pradesh","Arunachal Pradesh","Assam","Bihar","Chandigarh","Chhattisgarh", "Dadra and Nagar Haveli","Daman and Diu","Delhi","Goa","Gujarat","Haryana","Himachal Pradesh","Jammu and Kashmir", "Jharkhand","Karnataka","Kerala","Ladakh","Lakshadweep","Madhya Pradesh","Maharashtra","Manipur","Meghalaya", "Mizoram","Nagaland","Odisha","Puducherry","Punjab","Rajasthan","Sikkim","Tamil Nadu","Telangana","Tripura","Uttar Pradesh","Uttarakhand","West Bengal"] states_dict = {"Delhi":"New Delhi","Gujarat":"Surat","Haryana":"Gurgaon", "Karnataka":"Bangalore", "Karnataka":"Bengaluru", "Maharashtra":"Pune","Maharashtra":"Mumbai","Maharashtra":"Navi Mumbai","Telangana":"Hyderabad","West Bengal":"Kolkata", "Gujarat":"Surat","Rajasthan":"Kota","Rajasthan":"Jodhpur","Karnataka":"Bengaluru South","Uttar Pradesh":"Lukhnow","Uttar Pradesh":"Noida","Bihar":"Patna","Uttarakhand":"Dehradun","Madhya Pradesh":"Indore" , "Madhya Pradesh":"Bhopal", "Andaman and Nicobar Islands":"Andaman and Nicobar Islands", "Andhra Pradesh":"Andhra Pradesh","Arunachal Pradesh":"Arunachal Pradesh","Assam":"Assam","Bihar":"Bihar", "Chandigarh":"Chandigarh","Chhattisgarh":"Chhattisgarh", "Dadra and Nagar Haveli": "Dadra and Nagar Haveli","Daman and Diu":"Daman and Diu","Delhi":"Delhi", "Goa":"Goa","Gujarat":"Gujarat","Haryana":"Haryana","Himachal Pradesh":"Himachal Pradesh","Jammu and Kashmir":"Jammu and Kashmir", "Jharkhand": "Jharkhand", "Karnataka":"Karnataka","Kerala":"Kerala","Ladakh":"Ladakh","Lakshadweep":"Lakshadweep","Madhya Pradesh":"Madhya Pradesh","Maharashtra":"Maharashtra", "Odisha":"Odisha","Puducherry":"Puducherry","Punjab":"Punjab","Rajasthan":"Rajasthan","Tamil Nadu":"Tamil Nadu","Telangana":"Telangana","Uttar Pradesh":"Uttar Pradesh", "Uttarakhand":"Uttarakhand","West Bengal":"West Bengal","West Bengal":"Calcutta","Uttar Pradesh":"Lucknow" } abv = x.split(',')[-1].lstrip() state_name = x.split(',')[0].lstrip() if abv in states: state = abv else: if state_name in states_dict.values(): state = list(states_dict.keys())[list(states_dict.values()).index(state_name)] else: state = 'Non_India' return state # create abreviated states column df2 = df1.copy() df2['states'] = df1['location'].apply(get_state) # extract total sentiment per state df_state_sentiment = df2.groupby(['states'])['Label'].value_counts().unstack().fillna(0.0).reset_index() df_state_sentiment['total_sentiment'] = -(df_state_sentiment[0])+df_state_sentiment[1] dff = df_state_sentiment[df_state_sentiment['states'] != 'Non_India'] folium.Choropleth(geo_data=geojsonData, data=dff, name='CHOROPLETH', key_on='feature.id', columns = ['states','total_sentiment'], fill_color='YlOrRd', fill_opacity=0.7, line_opacity=0.4, legend_name='Sentiments', highlight=True).add_to(map_choropleth_high_public) folium.LayerControl().add_to(map_choropleth_high_public) #display(map_choropleth_high_public) st.sidebar.header("Map Visualisation") if not st.sidebar.checkbox("Close", True, key='4'): folium_static(map_choropleth_high_public) if __name__ == '__main__': run()