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("

🗨️Twitter Sentiment Analysis App

", 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("Preview Dataset",unsafe_allow_html=True) st.dataframe(df,use_container_width=True,height = 200) st.write ("Jumlah Tweet: ",df.shape[0]) # download datasets st.write("

📊 Analisis Sentimen

",unsafe_allow_html=True) st.caption("double tap untuk mereset grafik") col_fig1, col_fig2 = st.columns([4,3]) with col_fig1: with st.spinner('Sedang Membuat Grafik...'): st.write("Jumlah Tweet Tiap Sentiment",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("Wordcloud Tiap Sentiment",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("

✨ Sentiment Clustering

",unsafe_allow_html=True) st.caption("double tap untuk mereset grafik") @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")