Summary of Sms Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing, by Dare Azeez Oyeyemi et al.
SMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing
by Dare Azeez Oyeyemi, Adebola K. Ojo
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research addresses the pervasive issue of SMS spam, which poses threats to users’ privacy and security. The study introduces a novel approach utilizing Natural Language Processing (NLP) and machine learning models, particularly BERT (Bidirectional Encoder Representations from Transformers), for SMS spam detection and classification. The approach involves data preprocessing techniques, such as stop word removal and tokenization, along with feature extraction using BERT. Machine learning models, including SVM, Logistic Regression, Naive Bayes, Gradient Boosting, and Random Forest, are integrated with BERT for differentiating spam from ham messages. The evaluation results revealed that the Naïve Bayes classifier + BERT model achieves the highest accuracy at 97.31% with the fastest execution time of 0.3 seconds on the test dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SMS spam is a major problem that threatens users’ privacy and security through phishing and fraud. Despite existing spam filtering techniques, there is still a need for a more effective solution to address this issue. The proposed research introduces a novel approach using NLP and machine learning models, including BERT, to detect and classify SMS spam messages. The approach involves preprocessing the data and extracting features using BERT before applying machine learning models to differentiate between spam and ham messages. The results show that the Naïve Bayes classifier + BERT model achieves high accuracy and speed in detecting spam. |
Keywords
» Artificial intelligence » Bert » Boosting » Classification » Encoder » Feature extraction » Logistic regression » Machine learning » Naive bayes » Natural language processing » Nlp » Random forest » Tokenization
