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import streamlit as st
import pandas as pd
import script.functions as fn
import plotly.express as px
import matplotlib.pyplot as plt
# import text_proc in script folder
import script.text_proc as tp
from sentence_transformers import SentenceTransformer
# Load data
# add tiwtter logo inside title
st.markdown("<h1 style='text-align: center;'>📚 Twitter Sentiment Analysis App</h1>", unsafe_allow_html=True)
st.write("Aplikasi sederhana untuk melakukan analisis sentimen terhadap tweet yang diinputkan dan mengekstrak topik dari setiap sentimen.")
# streamlit selectbox simple and advanced
sb1,sb2 = st.columns([1,4])
with sb1:
option = st.selectbox('Pilih Mode Pencarian',('Simple','Advanced'))
if option == 'Simple':
# create col1 and col2
col1, col2 = st.columns([3,2])
with col1:
input = st.text_input("Masukkan User/Hastag", "@traveloka")
with col2:
length = st.number_input("Jumlah Tweet", 10, 500, 100)
else :
col1, col2 = st.columns([3,1])
with col1:
input = st.text_input("Masukkan Parameter Pencarian", "(to:@traveloka AND @traveloka) -filter:links filter:replies lang:id")
with col2:
length = st.number_input("Jumlah Tweet", 10, 500, 100)
st.caption("anda bisa menggunakan parameter pencarian yang lebih spesifik, parameter ini sama dengan paremeter pencarian di twitter")
submit = st.button("🔍Cari Tweet")
st.caption("semakin banyak tweet yang diambil maka semakin lama proses analisis sentimen")
if submit:
# df = pd.read_csv("assets/data.csv")
with st.spinner('Mengambil data dari twitter... (1/2)'):
df = fn.get_tweets(input, length, option)
with st.spinner('Melakukan Prediksi Sentimen... (2/2)'):
df = fn.get_sentiment(df)
df.to_csv('assets/data.csv',index=False)
# plot
st.write("<b>Preview Dataset</b>",unsafe_allow_html=True)
def color_sentiment(val):
color_dict = {"positif": "#00cc96", "negatif": "#ef553b","netral": "#636efa"}
return f'color: {color_dict[val]}'
st.dataframe(df.style.applymap(color_sentiment, subset=['sentiment']),use_container_width=True,height = 200)
# st.dataframe(df,use_container_width=True,height = 200)
st.write ("Jumlah Tweet: ",df.shape[0])
# download datasets
st.write("<h3>📊 Analisis Sentimen</h3>",unsafe_allow_html=True)
col_fig1, col_fig2 = st.columns([4,3])
with col_fig1:
with st.spinner('Sedang Membuat Grafik...'):
st.write("<b>Jumlah Tweet Tiap Sentiment</b>",unsafe_allow_html=True)
fig_1 = fn.get_bar_chart(df)
st.plotly_chart(fig_1,use_container_width=True,theme="streamlit")
with col_fig2:
st.write("<b>Wordcloud Tiap Sentiment</b>",unsafe_allow_html=True)
tab1,tab2,tab3 = st.tabs(["😞 negatif","😐 netral","😃 positif"])
with tab1:
wordcloud_pos = tp.get_wordcloud(df,"negatif")
fig = plt.figure(figsize=(10, 5))
plt.imshow(wordcloud_pos, interpolation="bilinear")
plt.axis("off")
st.pyplot(fig)
with tab2:
wordcloud_neg = tp.get_wordcloud(df,"netral")
fig = plt.figure(figsize=(10, 5))
plt.imshow(wordcloud_neg, interpolation="bilinear")
plt.axis("off")
st.pyplot(fig)
with tab3:
wordcloud_net = tp.get_wordcloud(df,"positif")
fig = plt.figure(figsize=(10, 5))
plt.imshow(wordcloud_net, interpolation="bilinear")
plt.axis("off")
st.pyplot(fig)
st.write("<h3>✨ Sentiment Clustering</h3>",unsafe_allow_html=True)
@st.experimental_singleton
def load_sentence_model():
embedding_model = SentenceTransformer('sentence_bert')
return embedding_model
embedding_model = load_sentence_model()
tab4,tab5,tab6 = st.tabs(["😞 negatif","😐 netral","😃 positif"])
with tab4:
if len(df[df["sentiment"]=="negatif"]) < 11:
st.write("Tweet Terlalu Sedikit, Tidak dapat melakukan clustering")
st.write(df[df["sentiment"]=="negatif"])
else:
with st.spinner('Sedang Membuat Grafik...(1/2)'):
text,data,fig = tp.plot_text(df,"negatif",embedding_model)
st.plotly_chart(fig,use_container_width=True,theme=None)
with st.spinner('Sedang Mengekstrak Topik... (2/2)'):
fig,topic_modelling = tp.topic_modelling(text,data)
st.plotly_chart(fig,use_container_width=True,theme="streamlit")
with tab5:
if len(df[df["sentiment"]=="netral"]) < 11:
st.write("Tweet Terlalu Sedikit, Tidak dapat melakukan clustering")
st.write(df[df["sentiment"]=="netral"])
else:
with st.spinner('Sedang Membuat Grafik... (1/2)'):
text,data,fig = tp.plot_text(df,"netral",embedding_model)
st.plotly_chart(fig,use_container_width=True,theme=None)
with st.spinner('Sedang Mengekstrak Topik... (2/2)'):
fig,topic_modelling = tp.topic_modelling(text,data)
st.plotly_chart(fig,use_container_width=True,theme="streamlit")
with tab6:
if len(df[df["sentiment"]=="positif"]) < 11:
st.write("Tweet Terlalu Sedikit, Tidak dapat melakukan clustering")
st.write(df[df["sentiment"]=="positif"])
else:
with st.spinner('Sedang Membuat Grafik...(1/2)'):
text,data,fig = tp.plot_text(df,"positif",embedding_model)
st.plotly_chart(fig,use_container_width=True,theme=None)
with st.spinner('Sedang Mengekstrak Topik... (2/2)'):
fig,topic_modelling = tp.topic_modelling(text,data)
st.plotly_chart(fig,use_container_width=True,theme="streamlit")
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