| 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. | |
| """ | |
| ) | |