In the modern era, mobile phones have become ubiquitous, and Short Message
Service (SMS) has grown to become a multi-million-dollar service due to the
widespread adoption of mobile devices and the millions of people who use SMS
daily. However, SMS spam has also become a pervasive problem that endangers
users' privacy and security through phishing and fraud. Despite numerous spam
filtering techniques, there is still a need for a more effective solution to
address this problem [1]. This research addresses the pervasive issue of SMS
spam, which poses threats to users' privacy and security. Despite existing spam
filtering techniques, the high false-positive rate persists as a challenge. 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.
Data preprocessing techniques, such as stop word removal and tokenization, are
applied, 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.
Evaluation results revealed that the Na\"ive Bayes classifier + BERT model
achieves the highest accuracy at 97.31% with the fastest execution time of 0.3
seconds on the test dataset. This approach demonstrates a notable enhancement
in spam detection efficiency and a low false-positive rate. The developed model
presents a valuable solution to combat SMS spam, ensuring faster and more
accurate detection. This model not only safeguards users' privacy but also
assists network providers in effectively identifying and blocking SMS spam
messages.

提出了利用自然语言处理和机器学习模型（特别是 BERT）进行短信垃圾检测和分类的方法，结果显示使用朴素贝叶斯分类器 + BERT 模型在测试数据集上实现了最高 97.31% 的准确率和最快 0.3 秒的执行时间，从而在短信垃圾检测效率和误报率方面有显著的改进。该模型为对抗短信垃圾提供了有价值的解决方案，不仅保护了用户的隐私，还帮助网络提供商有效识别和屏蔽短信垃圾消息。

利用自然语言处理技术对电话网络进行短信垃圾检测和分类以打击滥用行为

SMS Spam Detection and Classification to Combat Abuse in Telephone  Networks Using Natural Language Processing

This paper investigates the consequences of encoding a $K$-valued categorical
variable incorrectly as $K$ bits via one-hot encoding, when using a Na\"{\i}ve
Bayes classifier. This gives rise to a product-of-Bernoullis (PoB) assumption,
rather than the correct categorical Na\"{\i}ve Bayes classifier. The
differences between the two classifiers are analysed mathematically and
experimentally. In our experiments using probability vectors drawn from a
Dirichlet distribution, the two classifiers are found to agree on the maximum a
posteriori class label for most cases, although the posterior probabilities are
usually greater for the PoB case.

通过错误地将一个 $K$ 值分类变量编码为 $K$ 位二进制数，将其用于朴素贝叶斯分类器，本文研究了这种编码方式的后果，从而得出了一个伯努利乘积（PoB）假设，而不是正确的分类朴素贝叶斯分类器。通过数学和实验分析了两种分类器之间的差异。在从狄利克雷分布中抽取的概率向量实验中，大多数情况下，这两种分类器在最大后验类标签上达成一致，尽管 PoB 情况下的后验概率通常更大。