The SMS Spam Collection is a comprehensive dataset curated for spam detection research, offering 5,574 English SMS messages meticulously labeled as either spam or legitimate (ham). This dataset plays a pivotal role in advancing the field of mobile communication security and spam filtering technologies.
The collection aggregates data from several sources:
- Grumbletext Forum: 425 spam messages extracted from a UK-based complaint platform.
- NUS SMS Corpus: A subset of 3,375 legitimate messages contributed by Singaporean students.
- Caroline Tag’s Ph.D. Thesis: 450 ham messages.
- SMS Spam Corpus v0.1 Big: A mix of 1,002 ham and 322 spam messages.
Each entry consists of two columns: the label (spam/ham) and the raw text, facilitating diverse research in natural language processing and machine learning.
Researchers have leveraged this dataset to benchmark algorithms for spam detection, fostering advancements in predictive modeling and text classification. A prominent study analyzing this corpus is the 2011 ACM Symposium paper by Almeida, Gómez Hidalgo, and Yamakami.
Explore the dataset and develop your model to classify SMS messages accurately as spam or legitimate! Check this link.