File size: 3,562 Bytes
caf57e7 086edea 610814b ac896ff caf57e7 40f7988 0a4ef0e 5a1ffea ab20b35 0a4ef0e 5a1ffea 0a4ef0e 5a1ffea 086edea 9efaeb6 5a1ffea 0af20e7 112a4cf 0f2bd46 5a1ffea 6486a17 5a1ffea 73b4ec0 ad4de8d 5a1ffea 086edea 5a1ffea 4946181 5a1ffea 4946181 5a1ffea 4946181 5a1ffea 4946181 5a1ffea 37af0d2 5a1ffea 37af0d2 5a1ffea ad4de8d 5a1ffea 086edea 5a1ffea 4946181 5a1ffea ad4de8d 5a1ffea ad4de8d 73b4ec0 5a1ffea 086edea 5a1ffea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
import streamlit as st
import pandas as pd
import plotly_express as px
import plotly.graph_objects as go
from functions import *
import validators
st.set_page_config(page_title="Earnings Sentiment Analysis", page_icon="📈")
st.sidebar.header("Sentiment Analysis")
st.markdown("## Earnings Sentiment Analysis with FinBert-Tone")
if st.session_state.url or st.session_state.upload:
results, title = inference(st.session_state.url,st.session_state.upload)
st.subheader(title)
earnings_passages = results['text']
st.session_state['earnings_passages'] = 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 = sentiment_pipe(earnings_passages)
with st.expander("See Transcribed Earnings Text"):
st.write(f"Number of Sentences: {len(earnings_sentences)}")
st.write(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=25,
r=25,
b=25,
t=50,
pad=2
)
)
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_1 = go.Figure()
fig_1.add_trace(go.Indicator(
mode = "delta",
value = sentiment_score,
domain = {'row': 1, 'column': 1}))
fig_1.update_layout(
template = {'data' : {'indicator': [{
'title': {'text': "Sentiment Score"},
'mode' : "number+delta+gauge",
'delta' : {'reference': 50}}]
}},
autosize=False,
width=250,
height=250,
margin=dict(
l=5,
r=5,
b=5,
pad=2
)
)
with st.sidebar:
st.plotly_chart(fig_1)
## 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=True,
width=1000,
height=500,
margin=dict(
b=5,
t=50,
pad=4
)
)
st.plotly_chart(fig)
else:
st.write("No YouTube URL or file upload detected") |