Spaces:
Runtime error
Runtime error
File size: 9,690 Bytes
fccd4a8 d09b322 fccd4a8 f746f70 d09b322 f746f70 d09b322 fccd4a8 d09b322 fccd4a8 d09b322 fccd4a8 d09b322 fccd4a8 d09b322 fccd4a8 d09b322 fccd4a8 d09b322 fccd4a8 d09b322 fccd4a8 d09b322 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 |
import streamlit as st
st.set_page_config(
page_title="Social Media Sentiment Analyzer", page_icon="📊", layout="wide"
)
import pandas as pd
import helper_functions as hf
import plotly.express as px
import plotly.io as pio
import plotly
# Whenever the search button is clicked, the search_callback function is called
def search_callback_twitter():
if platform == "Twitter":
if len(st.session_state.search_term_twitter) == 0:
st.error("Please enter a search term")
return
try:
st.session_state.df = hf.get_tweets(st.session_state.search_term_twitter, st.session_state.num_tweets)
st.session_state.df = hf.get_sentiment(st.session_state.df)
except:
st.error("Please enter a valid search term")
return
def search_callback_youtube():
if platform == "Youtube":
if len(st.session_state.search_term_youtube) == 0:
st.error("Please enter a valid url")
return
try:
st.session_state.df = hf.get_youtube_comments(st.session_state.search_term_youtube, st.session_state.num_comments)
st.session_state.df = hf.get_sentiment_youtube(st.session_state.df)
except:
st.error("Please enter a valid url")
return
def twitter_form():
with st.form(key="search_form"):
st.subheader("Search Parameters")
st.text_input("Enter a User handle (like _@elonmusk_), Hashtag (like _#Bitcoin_) or Topic (like _climate change_)", key="search_term_twitter")
st.slider("Number of tweets", min_value=100, max_value=500, key="num_tweets")
st.form_submit_button(label="Search", on_click=search_callback_twitter)
st.markdown(
"Note: it may take a while to load the results, especially with large number of tweets"
)
def youtube_form():
with st.form(key="search_form"):
st.subheader("Search Parameters")
st.text_input("Enter a Video link to analyse comments", key="search_term_youtube")
st.slider("Number of Comments", min_value=100, max_value=500, key="num_comments")
st.form_submit_button(label="Search", on_click=search_callback_youtube)
st.markdown(
"Note: it may take a while to load the results, especially with large number of comments"
)
with st.sidebar:
st.title("Social Media Sentiment Analyzer")
#st.subheader("Choose your platform")
platform = st.radio(
"Choose your platform 👇",
["Twitter", "Youtube"],
# key="visibility",
# label_visibility=st.session_state.visibility,
# disabled=st.session_state.disabled,
horizontal=True,
)
if platform == "Twitter":
twitter_form()
if platform == "Youtube":
youtube_form()
st.markdown(
"<div style='position: fixed; bottom: 0;'>Created by Taaha Bajwa</div>",
unsafe_allow_html=True,
)
if "df" in st.session_state:
def make_dashboard(tweet_df, bar_color, wc_color):
# first row
col1, col2, col3 = st.columns([28, 34, 38])
with col1:
sentiment_plot = hf.plot_sentiment(tweet_df)
sentiment_plot.update_layout(height=350, title_x=0.5)
st.plotly_chart(sentiment_plot, theme=None, use_container_width=True)
with col2:
top_unigram = hf.get_top_n_gram(tweet_df, ngram_range=(1, 1), n=10)
unigram_plot = hf.plot_n_gram(
top_unigram, title="Top 10 Occuring Words", color=bar_color
)
unigram_plot.update_layout(height=350)
st.plotly_chart(unigram_plot, theme=None, use_container_width=True)
with col3:
top_bigram = hf.get_top_n_gram(tweet_df, ngram_range=(2, 2), n=10)
bigram_plot = hf.plot_n_gram(
top_bigram, title="Top 10 Occuring Bigrams", color=bar_color
)
bigram_plot.update_layout(height=350)
st.plotly_chart(bigram_plot, theme=None, use_container_width=True)
# second row
col1, col2 = st.columns([60, 40])
with col1:
def sentiment_color(sentiment):
if sentiment == "Positive":
return "background-color: #54A24B; color: white"
elif sentiment == "Negative":
return "background-color: #FF7F0E"
else:
return "background-color: #1F77B4"
st.dataframe(
tweet_df[["Sentiment", "Tweet"]].style.applymap(
sentiment_color, subset=["Sentiment"]
),
height=350,
)
with col2:
wordcloud = hf.plot_wordcloud(tweet_df, colormap=wc_color)
st.pyplot(wordcloud)
def make_dashboard_youtube(tweet_df, bar_color, wc_color):
tweet_df = tweet_df.rename(columns={"Comment": "Tweet"})
# first row
col1, col2, col3 = st.columns([28, 34, 38])
with col1:
sentiment_plot = hf.plot_sentiment(tweet_df)
sentiment_plot.update_layout(height=350, title_x=0.5)
st.plotly_chart(sentiment_plot, theme=None, use_container_width=True)
with col2:
top_unigram = hf.get_top_n_gram(tweet_df, ngram_range=(1, 1), n=10)
unigram_plot = hf.plot_n_gram(
top_unigram, title="Top 10 Occuring Words", color=bar_color
)
unigram_plot.update_layout(height=350)
st.plotly_chart(unigram_plot, theme=None, use_container_width=True)
with col3:
top_bigram = hf.get_top_n_gram(tweet_df, ngram_range=(2, 2), n=10)
bigram_plot = hf.plot_n_gram(
top_bigram, title="Top 10 Occuring Bigrams", color=bar_color
)
bigram_plot.update_layout(height=350)
st.plotly_chart(bigram_plot, theme=None, use_container_width=True)
# second row
col1, col2 = st.columns([60, 40])
with col1:
def sentiment_color(sentiment):
if sentiment == "Positive":
return "background-color: #54A24B; color: white"
elif sentiment == "Negative":
return "background-color: #FF7F0E"
else:
return "background-color: #1F77B4"
tweet_df_temp = tweet_df[["Sentiment", "Tweet"]]
tweet_df_temp = tweet_df_temp.rename(columns={"Tweet": "Comment"})
st.dataframe(
tweet_df_temp[["Sentiment", "Comment"]].style.applymap(
sentiment_color, subset=["Sentiment"]
),
height=350,
)
with col2:
wordcloud = hf.plot_wordcloud(tweet_df, colormap=wc_color, mask_url='static/yt_mask.png')
try:
st.pyplot(wordcloud)
except:
st.write("Wordcloud not available for this search term")
adjust_tab_font = """
<style>
button[data-baseweb="tab"] > div[data-testid="stMarkdownContainer"] > p {
font-size: 20px;
}
</style>
"""
st.write(adjust_tab_font, unsafe_allow_html=True)
if platform == "Twitter" and st.session_state.search_term_twitter != "":
try:
tab1, tab2, tab3, tab4 = st.tabs(["All", "Positive 😊", "Negative ☹️", "Neutral 😐"])
with tab1:
tweet_df = st.session_state.df
make_dashboard(tweet_df, bar_color="#1F77B4", wc_color="Blues")
with tab2:
tweet_df = st.session_state.df.query("Sentiment == 'Positive'")
make_dashboard(tweet_df, bar_color="#54A24B", wc_color="Greens")
with tab3:
tweet_df = st.session_state.df.query("Sentiment == 'Negative'")
make_dashboard(tweet_df, bar_color="#FF7F0E", wc_color="Oranges")
with tab4:
tweet_df = st.session_state.df.query("Sentiment == 'Neutral'")
make_dashboard(tweet_df, bar_color="#1F77B4", wc_color="Blues")
except:
st.error("No plots to display.")
elif platform == "Youtube" and st.session_state.search_term_youtube != "":
try:
tab1, tab2, tab3, tab4 = st.tabs(["All", "Positive 😊", "Negative ☹️", "Neutral 😐"])
with tab1:
tweet_df = st.session_state.df
if tweet_df.shape[0] > 0:
make_dashboard_youtube(tweet_df, bar_color="#1F77B4", wc_color="Blues")
else:
st.write("No comments found.")
with tab2:
tweet_df = st.session_state.df.query("Sentiment == 'Positive'")
if tweet_df.shape[0] > 0:
make_dashboard_youtube(tweet_df, bar_color="#54A24B", wc_color="Greens")
else:
st.write("No positive comments found.")
with tab3:
tweet_df = st.session_state.df.query("Sentiment == 'Negative'")
if tweet_df.shape[0] > 0:
make_dashboard_youtube(tweet_df, bar_color="#FF7F0E", wc_color="Oranges")
else:
st.write("No negative comments found.")
with tab4:
tweet_df = st.session_state.df.query("Sentiment == 'Neutral'")
if tweet_df.shape[0] > 0:
make_dashboard_youtube(tweet_df, bar_color="#1F77B4", wc_color="Blues")
else:
st.write("No neutral comments found.")
except:
st.error("No plots to display.") |