Sentiment Analysis Application for QRIS

Overview

This Flask-based web application performs sentiment analysis on public opinions about QRIS (Quick Response Code Indonesian Standard). The analysis results are presented through the website, showing sentiment analysis results from CSV files containing comments/opinions about QRIS, sentiment percentages in pie charts, and word clouds for each sentiment category.

Project Structure

Notebooks

Contains Jupyter notebooks for various stages of the sentiment analysis process from crawling to modeling.

Database

Stores data, trained models, and other database-related files used in the application.

Templates

HTML templates for rendering different pages of the web application.

Modules

Python modules containing backend logic and functions used in the Flask application.

Interface Implementation

Home Page

The first page users see upon accessing the system, providing general information about the application.

Sentiment Analysis Page

Allows users to upload a CSV file containing comments or opinions about QRIS for sentiment analysis. Users can view the sentiment analysis results on this page.

Dataset Page

Users can view the dataset used for training and testing purposes. Additionally, users have the option to add new data.

Training Page

Provides insights into the sentiment analysis training process, including preprocessing steps and TFIDF in a tabular format.

Evaluation Page

Displays sentiment analysis results from the training page, including analysis results from Logistic Regression and Lexicon-Based approaches in pie charts and word clouds. Users can also view method evaluations through evaluation metrics and confusion matrices.

Usage

Navigation

Navigate through the different pages to perform various tasks related to sentiment analysis for QRIS:

• Home: Provides general information about the application.
• Sentiment Analysis: Upload CSV files on this page for sentiment analysis tasks.
• Dataset: View and manage the dataset used for analysis.
• Training: Explore the sentiment analysis training process and TFIDF insights.
• Evaluation: View analysis results, including pie charts, word clouds, evaluation metrics, and confusion matrices.

Requirements

• Python 3.x
• Flask
• pandas
• scikit-learn
• matplotlib
• wordcloud

Installation and Setup

1. Clone the repository:

   git clone https://github.com/nbilasals/aplikasi-ta.git
   cd aplikasi-ta
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the Flask app:

   flask run
   

4. Access the application in your web browser at http://localhost:5001