GenAIDevTOProd/sms-spam-enriched

SMS Spam Enriched Dataset

An enriched version of the classic SMS Spam Collection Dataset from UC Irvine with additional engineered features and semantic embeddings.
This dataset is designed for spam detection, feature engineering experiments, and model interpretability research.

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Dataset Overview

• Total samples: 5,171
• Classes:
  • 0: Ham (non-spam)
  • 1: Spam

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Enrichments Added

Alongside the raw SMS text (sms) and labels (label), we engineered multiple new features:

1. char_len → Total characters in the message
2. word_count → Total words in the message
3. punct_score → Weighted score for punctuation usage (!, ?, ...)
4. spam_keywords → Count of known spammy tokens (free, win, urgent, etc.)
5. lexical_diversity → Ratio of unique words to total words
6. readability → Flesch Reading Ease score
7. caps_ratio → Proportion of uppercase characters
8. digit_ratio → Proportion of numeric digits
9. exclaim_ratio → Ratio of exclamation marks to total characters
10. url_flag → Binary indicator for presence of URLs
11. spammy_words → Count of flagged high-signal words
12. entropy → Shannon entropy of characters
13. embeddings → Sentence-transformer vector representations (for downstream tasks like clustering, semantic similarity, visualization)

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Example Row

sms	label	char_len	word_count	punct_score	spam_keywords	lexical_diversity	readability	caps_ratio	digit_ratio	url_flag	entropy
"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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Visualizations

Below is a PCA projection of SMS embeddings, showing clear separation between spam (red) and ham (blue):

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Benchmark Models

We trained baseline classifiers using the enriched dataset:

• Logistic Regression (with combined features)

  • Accuracy: ~99%
  • F1 (spam): ~0.95

• Random Forest (with combined features)

  • Accuracy: ~98%
  • F1 (spam): ~0.92

  Logistic Regression Report (Combined Features):

          precision    recall  f1-score   support
  
       0       0.99      1.00      0.99       904
       1       0.98      0.92      0.95       131
  

  accuracy 0.99 1035

  macro avg 0.99 0.96 0.97 1035

weighted avg 0.99 0.99 0.99 1035

Random Forest Report (Combined Features): precision recall f1-score support

       0       0.98      1.00      0.99       904
       1       0.99      0.86      0.92       131

accuracy                           0.98      1035

macro avg 0.99 0.93 0.96 1035

weighted avg 0.98 0.98 0.98 1035

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Use Cases

• Spam detection model training
• Feature engineering demonstration
• Embedding-based similarity and clustering tasks
• Educational material for NLP + ML pipelines

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Citation

If you use this dataset, please cite the original SMS Spam Collection dataset:

@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)", }.

Dataset Enrichment and Feature Engineering contributions by Naga Adithya Kaushik (GenAIDevTOProd).

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License

This dataset is distributed under the same terms as the original SMS Spam dataset (publicly available for research).

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