Datasets:

GenAIDevTOProd
/

sms-spam-enriched

dataset_info:
  features:
    - name: sms
      dtype: string
    - name: label
      dtype: int64
    - name: char_len
      dtype: int64
    - name: word_count
      dtype: int64
    - name: punct_score
      dtype: int64
    - name: spam_keywords
      dtype: int64
    - name: lexical_diversity
      dtype: float64
    - name: readability
      dtype: float64
    - name: caps_ratio
      dtype: float64
    - name: digit_ratio
      dtype: float64
    - name: exclaim_ratio
      dtype: float64
    - name: url_flag
      dtype: int64
    - name: spammy_words
      dtype: int64
    - name: entropy
      dtype: float64
  splits:
    - name: train
      num_bytes: 974514
      num_examples: 5171
  download_size: 446788
  dataset_size: 974514
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

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.

Dataset Overview

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

Enrichments Added

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

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

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

Visualizations

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

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

Use Cases

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

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).

License

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