This project encompasses various functionalities including data analysis, music genre classification, data preprocessing, and a sentiment analysis GUI.

• Utilized MapReduce to process large-scale music data, including preprocessing, data cleaning, and feature extraction.
• Developed sentiment prediction and genre classification based on TF-IDF calculation of lyrics using Bayes Classifier.
• Implemented a website interaction system separating backend calculations from the frontend GUI for user interaction.

- Analysis
- GenreClassification
- Preprocess
- Sentiment_gui

This module is dedicated to data analysis, providing tools for data processing and visualization.

This module focuses on music genre classification using machine learning algorithms to categorize music.

This module handles data preprocessing, including data cleaning and feature extraction.

This module offers a graphical user interface for sentiment analysis, allowing users to input text and receive sentiment analysis results.

Contributions are welcome! Please submit issues and pull requests.

To compile and run the Tower Defense game, follow these steps:

1. Linux Operating System

   • Install Linux: You can install a Linux OS like RHELS 7.0 directly or set up a Linux virtual machine on Windows.
   • Install SSH: Ensure SSH is installed for remote management of Hadoop nodes.

2. Java

   • Download and Install Java: For example, install Java in the /usr/java directory.
   • Configure Environment Variables:

     export JAVA_HOME=/usr/java/java-1.7
     export PATH=$JAVA_HOME/bin:$PATH

3. Hadoop

   • Download and Install Hadoop: Download the Hadoop package from the official website and extract it.
   • Configure Environment Variables:

     export HADOOP_HOME=/home/hadoop/hadoop_installs/hadoop-2.7.1
     export PATH=$HADOOP_HOME/bin:$PATH

   • Configure SSH for Passwordless Access:

     ssh-keygen -t rsa -P ""
     cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

4. Flask

   • Install Flask: Use pip to install Flask.

     pip install Flask

1. Clone the Repository:

• Open a terminal and run:

    git clone https://github.com/yun-ni-2024/Sentiment-and-Genre-Classifier.git
    cd Sentiment-and-Genre-Classifier

2. Navigate to the GUI Directory:

   • Change to the gui directory:

     cd gui

3. Run the Application:

   • Execute the app.py file:

     python app.py

4. Open the Web Application:

   • Open your web browser and go to http://localhost:8000.

5. Upload the Source Data Folder:

   • Upload the compressed source data folder, which should include the users, songs, lyrics, and genres files.

6. Interact with the Web Application:

   • Click the button on the webpage to proceed with the desired actions.

Here are some images to showcase the effects: