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