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| import streamlit as st | |
| 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 | |
| st.set_page_config( | |
| page_title="twitter sentiment analysis", | |
| page_icon="👋", | |
| ) | |
| st.sidebar.markdown("📚 Twitter Sentiment Analysis App") | |
| # 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([2,4]) | |
| with sb1: | |
| option = st.selectbox('Pilih Mode Pencarian',('Simple','Advanced')) | |
| with sb2: | |
| option_model = st.selectbox('Pilih Model',("IndoBERT (Accurate,Slow)",'Naive Bayes','Logistic Regression (Less Accurate,Fast)','XGBoost','Catboost','SVM','Random Forest')) | |
| 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, 10500, 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, 10500, 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: | |
| 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,option_model) | |
| 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) | |
| 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") | |