https://github.com/alrescha79-cmd/analisis-sentimen
Submission Proyek Analisis Sentimen IDCamp 2024
https://github.com/alrescha79-cmd/analisis-sentimen
analisis-sentimen dicoding-submission idcamp2024 machine-learning sentiment-analysis
Last synced: 8 months ago
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Submission Proyek Analisis Sentimen IDCamp 2024
- Host: GitHub
- URL: https://github.com/alrescha79-cmd/analisis-sentimen
- Owner: alrescha79-cmd
- License: mit
- Created: 2025-02-27T01:32:56.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-11T01:42:13.000Z (about 1 year ago)
- Last Synced: 2025-04-11T02:43:54.657Z (about 1 year ago)
- Topics: analisis-sentimen, dicoding-submission, idcamp2024, machine-learning, sentiment-analysis
- Language: Jupyter Notebook
- Homepage: https://www.dicoding.com/academysubmissions/3964343
- Size: 29.4 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Sentiment Analysis Project Submission
This project is part of the [Machine Learning Path Dicoding](https://www.dicoding.com/learningpaths/30). The goal of this project is to build a sentiment analysis model using various machine learning algorithms.
## Project Description
Sentiment analysis is the process of determining an individual's opinion or feelings about a topic based on provided text. In this project, various machine learning techniques are used to classify text into positive, negative, or neutral sentiments.
### Algorithms Used
1. **Decision Trees**: A tree-based supervised learning algorithm for classification tasks.
2. **Random Forest**: An ensemble learning method based on decision trees for higher accuracy.
3. **Support Vector Machines (SVM)**: A hyperplane-based algorithm for text classification.
4. **Bidirectional Long Short-Term Memory (Bi-LSTM)**: A deep learning approach to understand text context from both directions.
## Dataset
The dataset consists of labeled text data with positive, negative, or neutral sentiments. This dataset can be obtained from trusted sources such as Kaggle or other public datasets.
## How to Use
1. **Install Dependencies**
Ensure you have installed the required dependencies:
```bash
pip install -r requirements.txt
```
2. **Prepare the Dataset**
Place the dataset in the project directory and ensure its format matches the notebook's expected format.
3. **Train the Model**
Run the following notebook to train the model:
```bash
jupyter notebook
```
4. **Evaluate the Model**
The model will be evaluated using metrics such as accuracy, precision, recall, and F1-score.
5. **Make Predictions**
Use the trained model to make predictions on new data.
## Directory Structure
- `notebooks/`: Contains Jupyter Notebook files for preprocessing, training, and evaluating the model.
- `data/`: Directory where the dataset is stored.
- `models/`: Contains trained models for making predictions.
## Technologies Used
- Python
- Scikit-learn
- TensorFlow
- Keras
- Pandas
- Numpy
- Jupyter Notebook
## Contributing
Contributions are welcome! If you'd like to contribute, feel free to fork this repository, create a new branch, and submit a pull request.
```bash
git clone https://github.com/alrescha79-cmd/analisis-sentimen.git
git checkout -b new-feature
```
## Author
This project was created by:
- **[Anggun Caksono](https://www.github.com/alrescha79-cmd)**
If you encounter any issues or have questions, feel free to open a [new issue](https://github.com/alrescha79-cmd/analisis-sentimen/issues).
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