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| import streamlit as st | |
| import eda | |
| import model | |
| import conclusion | |
| # Sidebar | |
| st.sidebar.header("Choose Here!") | |
| options = ['Home Page', 'Exploratory Data Analysis', 'Test our Model!', 'Conclusion'] | |
| page = st.sidebar.selectbox(label='Select Page:', options=options) | |
| # Home Page | |
| if page == 'Home Page': | |
| st.header('Feedback to Foresight: Simplifying App Review Sentiment Analysis') | |
| st.caption("This project was carried out as part of Hacktiv8's Data Science programme final collaborative project.") | |
| st.caption('Please check our github repository [here!](https://github.com/devinlee14/FTDS-009-HCK-group-002)') | |
| st.markdown('---') | |
| st.markdown(''' | |
| #### Group members: | |
| * Devin Yaung Lee — Data Analyst | |
| * Fernaldy Aristo Wirjowerdojo — Data Engineer | |
| * Muhammad Furqon Pakpahan — Data Engineer | |
| * Sifra Hilda Juliana Siregar — Data Scientist | |
| ''') | |
| st.write('') | |
| st.caption('Please select another page in the `Select Page` on the left side of your screen to get started!') | |
| st.write('') | |
| with st.expander("Project Overview"): | |
| st.caption(''' | |
| This project focuses on performing sentiment analysis on Google Play Store app reviews. | |
| Utilizing Natural Language Processing (NLP), the goal is to analyse user feedback | |
| to gain insights into satisfaction and app perception. | |
| ''') | |
| with st.expander("Problem Statement"): | |
| st.caption(''' | |
| In the competitive landscape of mobile applications, user feedback for app reviews is a | |
| goldmine of insights that can inform product development and marketing strategies. | |
| However, these reviews are often unstructured, making it challenging to efficiently extract, | |
| categorize, and analyze sentiments and opinions. There is a need for an automated system | |
| that can process this feedback to provide actionable insights, identify trends in user sentiment, | |
| and highlight areas for improvement. This project aims to address the lack of structured | |
| analysis of user-generated content in app reviews on the Google Play Store, which, | |
| if leveraged correctly, can significantly enhance user satisfaction and app performance in the market. | |
| ''') | |
| with st.expander("Objectives"): | |
| st.caption(''' | |
| * **Develop an Automated Sentiment Analysis Model** | |
| Build and train a TensorFlow model to classify app reviews into positive, negative, and neutral sentiments with high accuracy. | |
| * **Understand the User Feedbacks in Depth** | |
| Utilize the sentiment analysis model to delve into the nuances of user feedback on the Google Play Store. | |
| ''') | |
| # EDA | |
| elif page == 'Exploratory Data Analysis': | |
| eda.run() | |
| # Model | |
| elif page == 'Test our Model!': | |
| model.run() | |
| # Conclusion | |
| else: | |
| conclusion.run() | |