Update pages/1_Earnings_Sentiment_Analysis_📈_.py

pages/1_Earnings_Sentiment_Analysis_📈_.py

@@ -1,4 +1,7 @@
 import streamlit as st
+import pandas as pd
+import plotly_express as px
+import plotly.graph_objects as go
 
 st.set_page_config(page_title="Earnings Sentiment Analysis", page_icon="📈")
 st.sidebar.header("Sentiment Analysis")
@@ -11,6 +14,89 @@ st.subheader(title)
 
 earnings_passages = results['text']
 
+with st.expander("See Transcribed Earnings Text"):
+    st.write(earnings_passages)
+
 with open('earnings.txt','w') as f:
     f.write(earnings_passages)
     
+with open('earnings.txt','r') as f:
+    earnings_passages = f.read()
+    
+earnings_sentiment, earnings_sentences = sent_pipe(earnings_passages)
+
+## Save to a dataframe for ease of visualization
+sen_df = pd.DataFrame(earnings_sentiment)
+sen_df['text'] = earnings_sentences
+grouped = pd.DataFrame(sen_df['label'].value_counts()).reset_index()
+grouped.columns = ['sentiment','count']
+
+# Display number of positive, negative and neutral sentiments
+fig = px.bar(grouped, x='sentiment', y='count', color='sentiment', color_discrete_map={"Negative":"firebrick","Neutral":\
+                                                                                       "navajowhite","Positive":"darkgreen"},\
+                                                                                       title='Earnings Sentiment')
+
+fig.update_layout(
+	showlegend=False,
+    autosize=True,
+    margin=dict(
+        l=50,
+        r=50,
+        b=50,
+        t=50,
+        pad=4
+    )
+)
+
+st.plotly_chart(fig)
+
+## Display sentiment score
+pos_perc = grouped[grouped['sentiment']=='Positive']['count'].iloc[0]*100/sen_df.shape[0]
+neg_perc = grouped[grouped['sentiment']=='Negative']['count'].iloc[0]*100/sen_df.shape[0]
+neu_perc = grouped[grouped['sentiment']=='Neutral']['count'].iloc[0]*100/sen_df.shape[0]
+
+sentiment_score = neu_perc+pos_perc-neg_perc
+
+fig = go.Figure()
+
+fig.add_trace(go.Indicator(
+    mode = "delta",
+    value = sentiment_score,
+    domain = {'row': 1, 'column': 1}))
+
+fig.update_layout(
+	template = {'data' : {'indicator': [{
+        'title': {'text': "Sentiment score"},
+        'mode' : "number+delta+gauge",
+        'delta' : {'reference': 50}}]
+                         }},
+    autosize=False,
+    width=400,
+    height=500,
+    margin=dict(
+        l=20,
+        r=50,
+        b=50,
+        pad=4
+    )
+)
+
+## Display negative sentence locations
+fig = px.scatter(sen_df, y='label', color='label', size='score', hover_data=['text'], color_discrete_map={"Negative":"firebrick","Neutral":"navajowhite","Positive":"darkgreen"}, title='Sentiment Score Distribution')
+
+
+fig.update_layout(
+	showlegend=False,
+    autosize=False,
+    width=1000,
+    height=500,
+    margin=dict(
+        l=50,
+        r=50,
+        b=50,
+        t=50,
+        pad=4
+    )
+)
+
+st.plotly_chart(fig)