Create pages/1_Tweets_Visualization_🔎_.py

pages/1_Tweets_Visualization_🔎_.py

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+from variables import *
+import plotly_express as px
+from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
+import matplotlib.pyplot as plt
+import streamlit as st
+import numpy as np
+import pandas as pd
+import textwrap
+
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+#st.set_page_config(page_title="Earnings Sentiment Analysis", page_icon="📈")
+st.sidebar.header("Sentiment Analysis Visualization")
+st.markdown("## Sentiment Analysis and Density Graphs")
+
+max_word = st.sidebar.slider(label= "WordCloud Max Words", min_value=20, max_value=500, value=50)
+max_font = st.sidebar.slider(label = "WordCloud Max Font", min_value=50, max_value=350, value=50)
+
+
+stopwords = set(STOPWORDS)
+stopwords.update(['us', 'one', 'will', 'said', 'now', 'well', 'man', 'may',
+    'little', 'say', 'must', 'way', 'long', 'yet', 'mean',
+    'put', 'seem', 'asked', 'made', 'half', 'much',
+    'certainly', 'might', 'came','RT','amp'])
+
+def cloud(text, max_word, max_font, random):
+    '''Generate Word Cloud'''
+    
+    wc = WordCloud(background_color="white", colormap="hot", max_words=max_word,
+    stopwords=stopwords, max_font_size=max_font, random_state=random).generate(text)
+
+    return wc
+    
+try:
+
+    if 'tdf' in st.session_state:
+        
+        df = st.session_state['tdf']
+        # df['creation_date'] = pd.to_datetime(df['creation_date'], 
+        #                                format='%Y-%m-%d %H:%M:%S-%Z', 
+        #                                errors='coerce').dt.date
+
+        with st.comtainer():
+            st.subheader('Sentiment Scatter Plot')
+            ## Display negative sentence locations
+            ht = df.tweet.apply(lambda txt: '<br>'.join(textwrap.wrap(txt, width=70)))
+            fig = px.scatter(df, y='sentiment', x='creation_time', color='topic', size='sentiment_confidence', hover_data=[ht,'topic'], \
+                             color_discrete_map={"Bearish":"firebrick","Neutral":"navajowhite","Bullish":"darkgreen"}, \
+                             title='Sentiment Score Distribution')
+        
+            fig.update_layout(
+            	showlegend=False,
+                autosize=True,
+                width=1000,
+                height=500,
+                margin=dict(
+                    b=5,
+                    t=50,
+                    pad=2
+                )
+            )
+        
+            st.plotly_chart(fig)
+
+        with st.comtainer():
+            st.subheader('Topic Distribution Scatter Plot')
+            ## Display negative sentence locations
+            ht = df.tweet.apply(lambda txt: '<br>'.join(textwrap.wrap(txt, width=70)))
+            fig = px.scatter(df, y='topic', x='creation_time', color='sentiment', size='topic_confidence', hover_data=[ht,'sentiment'],\
+                             title='Topic Score Distribution')
+        
+            fig.update_layout(
+            	showlegend=False,
+                autosize=True,
+                width=1000,
+                height=500,
+                margin=dict(
+                    b=5,
+                    t=50,
+                    pad=2
+                )
+            )
+        
+            st.plotly_chart(fig)
+
+        with st.container():
+            st.subheader('Topic Density Heatmap')
+            fig = px.density_heatmap(df, x='creation_time', y='topic')
+            st.plotly_chart(fig)
+    
+        with st.container():
+            st.subheader('Sentiment WordCloud')
+            cleaned_tweets = "".join(df['tweet'].tolist())
+            wc = cloud(cleaned_tweets, max_word, max_font, 35)
+            plt.imshow(wc, interpolation='bilinear')
+            plt.axis("off")
+            plt.show()
+            st.pyplot()
+            
+    else:
+    
+        st.warning("No Tweets detected, please navigate to Home page and refresh tweet stream",icon="⚠️")
+        
+except (AttributeError, KeyError):
+
+    st.error('Tweets Error, please navigate to Home page and refresh tweet stream', icon="🚨")