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