Sentiment Analysis Java is a Java-based project designed to analyze the sentiments of text data. This project showcases the use of Java for data processing, feature extraction, and machine learning model implementation in the context of sentiment analysis. It serves as a beginner-friendly demonstration of Java programming skills for a natural language processing task.
The project aims to provide a comprehensive overview of sentiment analysis techniques and their implementation in Java, making it a valuable resource for anyone looking to learn about sentiment analysis or improve their Java programming skills with a focus on simplicity and effectiveness.
Sentiment Analysis Java offers a practical and educational approach to sentiment analysis, covering the following key aspects:
- Data Processing: Techniques for cleaning and preprocessing text data, including tokenization, stemming, and stop word removal.
- Feature Extraction: Methods for converting text data into numerical features suitable for machine learning models, such as bag-of-words, TF-IDF, and word embeddings.
- Model Implementation: Implementation of popular machine learning algorithms for sentiment analysis, including Naive Bayes, Support Vector Machines, and Deep Learning models.
- Evaluation: Metrics and techniques for evaluating the performance of sentiment analysis models, such as accuracy, precision, recall, and F1-score.
To get started with the project, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/sentiment-analysis-java.git - Install the required dependencies (e.g., Java Development Kit, Apache Maven, etc.).
- Build the project using Maven:
mvn clean install - Run the main class to train and evaluate the sentiment analysis models.
Contributions to the project are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.