Spaces:
Build error
Build error
File size: 3,775 Bytes
02a7dcf dbd544a 02a7dcf 36374f8 02a7dcf 36374f8 02a7dcf 0304dc4 02a7dcf 36374f8 1986c70 02a7dcf 36374f8 02a7dcf 364f083 02a7dcf |
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 |
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.container():
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='sentiment', size='sentiment_confidence', hover_data=[ht,'topic','username'], \
color_discrete_map={"Bearish":"firebrick","Neutral":"navajowhite","Bullish":"darkgreen"}, \
title='Sentiment Score Distribution')
fig.update_layout(
showlegend=False,
autosize=True,
width=900,
height=500,
margin=dict(
b=5,
t=50,
pad=2
)
)
st.plotly_chart(fig)
with st.container():
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,'topic','username'],\
color_discrete_map={"Bearish":"firebrick","Neutral":"navajowhite","Bullish":"darkgreen"},\
title='Topic Score Distribution')
fig.update_layout(
showlegend=False,
autosize=True,
width=900,
height=500,
margin=dict(
b=5,
t=50,
pad=2
)
)
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()
except (AttributeError, KeyError) as e:
print(e)
st.error('Tweets Error, please navigate to Home page and refresh tweet stream', icon="🚨") |