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--- |
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dataset_info: |
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features: |
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- name: sms |
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dtype: string |
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- name: label |
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dtype: int64 |
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- name: char_len |
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dtype: int64 |
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- name: word_count |
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dtype: int64 |
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- name: punct_score |
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dtype: int64 |
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- name: spam_keywords |
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dtype: int64 |
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- name: lexical_diversity |
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dtype: float64 |
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- name: readability |
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dtype: float64 |
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- name: caps_ratio |
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dtype: float64 |
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- name: digit_ratio |
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dtype: float64 |
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- name: exclaim_ratio |
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dtype: float64 |
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- name: url_flag |
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dtype: int64 |
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- name: spammy_words |
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dtype: int64 |
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- name: entropy |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 974514 |
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num_examples: 5171 |
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download_size: 446788 |
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dataset_size: 974514 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# SMS Spam Enriched Dataset |
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An enriched version of the classic **SMS Spam Collection Dataset from UC Irvine** with additional engineered features and semantic embeddings. |
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This dataset is designed for **spam detection, feature engineering experiments, and model interpretability research**. |
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--- |
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## Dataset Overview |
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- **Total samples**: 5,171 |
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- **Classes**: |
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- `0`: Ham (non-spam) |
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- `1`: Spam |
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--- |
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## Enrichments Added |
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Alongside the raw SMS text (`sms`) and labels (`label`), we engineered multiple new features: |
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1. **char_len** → Total characters in the message |
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2. **word_count** → Total words in the message |
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3. **punct_score** → Weighted score for punctuation usage (`!`, `?`, `...`) |
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4. **spam_keywords** → Count of known spammy tokens (`free`, `win`, `urgent`, etc.) |
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5. **lexical_diversity** → Ratio of unique words to total words |
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6. **readability** → Flesch Reading Ease score |
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7. **caps_ratio** → Proportion of uppercase characters |
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8. **digit_ratio** → Proportion of numeric digits |
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9. **exclaim_ratio** → Ratio of exclamation marks to total characters |
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10. **url_flag** → Binary indicator for presence of URLs |
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11. **spammy_words** → Count of flagged high-signal words |
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12. **entropy** → Shannon entropy of characters |
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13. **embeddings** → Sentence-transformer vector representations (for downstream tasks like clustering, semantic similarity, visualization) |
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--- |
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## Example Row |
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| sms | label | char_len | word_count | punct_score | spam_keywords | lexical_diversity | readability | caps_ratio | digit_ratio | url_flag | entropy | |
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|------------------------------------------------|-------|----------|------------|-------------|---------------|-------------------|-------------|------------|-------------|----------|---------| |
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| "Free entry in 2 a wkly comp to win FA Cup..." | 1 | 156 | 28 | 0 | 3 | 0.857 | 80.83 | 0.064 | 0.16 | 0 | 4.69 | |
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--- |
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## Visualizations |
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Below is a **PCA projection** of SMS embeddings, showing clear separation between spam (red) and ham (blue): |
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--- |
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## Benchmark Models |
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We trained baseline classifiers using the enriched dataset: |
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- **Logistic Regression (with combined features)** |
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- Accuracy: ~99% |
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- F1 (spam): ~0.95 |
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- **Random Forest (with combined features)** |
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- Accuracy: ~98% |
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- F1 (spam): ~0.92 |
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Logistic Regression Report (Combined Features): |
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precision recall f1-score support |
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0 0.99 1.00 0.99 904 |
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1 0.98 0.92 0.95 131 |
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accuracy 0.99 1035 |
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macro avg 0.99 0.96 0.97 1035 |
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weighted avg 0.99 0.99 0.99 1035 |
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Random Forest Report (Combined Features): |
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precision recall f1-score support |
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0 0.98 1.00 0.99 904 |
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1 0.99 0.86 0.92 131 |
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accuracy 0.98 1035 |
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macro avg 0.99 0.93 0.96 1035 |
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weighted avg 0.98 0.98 0.98 1035 |
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--- |
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## Use Cases |
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- Spam detection model training |
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- Feature engineering demonstration |
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- Embedding-based similarity and clustering tasks |
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- Educational material for NLP + ML pipelines |
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--- |
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## Citation |
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If you use this dataset, please cite the original SMS Spam Collection dataset: |
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> @inproceedings{Almeida2011SpamFiltering, title={Contributions to the Study of SMS Spam Filtering: New Collection and Results}, author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami}, year={2011}, booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)", }. |
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> Dataset Enrichment and Feature Engineering contributions by Naga Adithya Kaushik (GenAIDevTOProd). |
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--- |
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## License |
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This dataset is distributed under the same terms as the original SMS Spam dataset (publicly available for research). |
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--- |
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