| --- |
| language: id |
| license: mit |
| tags: |
| - sentiment-analysis |
| - aspect-based-sentiment-analysis |
| - bert |
| - focal-loss |
| - pytorch |
| datasets: |
| - Reddit |
| metrics: |
| - f1 |
| base_model: |
| - google-bert/bert-base-uncased |
| --- |
| |
| # Aspect-Based Sentiment Analysis for Game Comments |
|
|
| This is a BERT-based classifier for performing **aspect-based sentiment analysis (ABSA)** on user comments about video games. |
| Each prediction considers both the **aspect** (topic/feature being discussed) and the **comment text** as inputs, and classifies the sentiment into 3 categories: |
| - **Negative** |
| - **Neutral** |
| - **Positive** |
|
|
| ## 🔍 How the Model Works |
| The model input consists of two segments: |
| - **Aspect** (the topic whose sentiment you want to evaluate) |
| - **Comment Text** (the actual user comment) |
|
|
| These are separated by a `[SEP]` token according to the BERT input format. |
| The model is trained using **Focal Loss** to handle class imbalance. |
|
|
| ## 📊 Dataset |
| The dataset used for training consists of user comments on video games with the following columns: |
| - `comment_text` |
| - `aspect` |
| - `sentiment` (0 = Negative, 1 = Neutral, 2 = Positive) |
| - `Dataset link` : https://huggingface.co/datasets/alwanrahmana/Aspect-based-sentiment-analysis |
|
|
| ## 📈 Performance |
| The model was trained using 5-Fold Cross Validation. |
| Evaluation metrics include **accuracy** and **F1-score**, with per-aspect breakdowns. |
|
|
| ## 🚀 How to Use |
|
|
| ### Install dependencies: |
| ```bash |
| pip install transformers torch |
