import streamlit as st from st_pages import Page, show_pages st.set_page_config(page_title="Sentiment Analysis", page_icon="🏠") show_pages( [ Page("streamlit_app.py/Homepage.py", "Home", "🏠"), Page( "streamlit_app.py/pages/Sentiment_Analysis.py", "Sentiment Analysis", "📝" ), ] ) st.title("Final Project in Machine Learning Course - Sentiment Analysis") st.markdown( """ **Team members:** | Student ID | Full Name | | ---------- | ------------------------ | | 19120600 | Bùi Nguyên Nghĩa | | 20120089 | Lê Xuân Hoàng | | 20120422 | Nguyễn Thị Ánh Tuyết | | 20120460 | Lê Nguyễn Hải Dương | | 20120494 | Lê Xuân Huy | """ ) st.header("The Need for Sentiment Analysis") st.markdown( """ Sentiment analysis algorithms are used to detect sentiment in a comment or a review. It is said that around 90% of consumers read online reviews before visiting a business or buying a product. These reviews can be positive or negative or neutral, and it is important to know what the customers are saying about your business. """ ) st.header("Technology used") st.markdown( """ In this demo, we used BERT as the model for sentiment analysis. BERT is a transformer-based model that was proposed in 2018 by Google. It is a pre-trained model that can be used for various NLP tasks such as sentiment analysis, question answering, etc. """ )