Structural learning framework for binary short text classification

Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown promising results in addressing the challenges of semantic sparsity and limited labeled data in short text classification. However, existing models have certain limitations. They rely on explicit data augmentation techniques to generate contrastive views, resulting in semantic corruption and noise. Additionally, these models only focus on learning the intrinsic consistency between the generated views, neglecting valuable discriminative information from other potential views. To address these issues, we propose a Simple graph contrastive learning framework for Short Text Classification (SimSTC). Our approach involves performing graph learning on multiple text-related component graphs to obtain multi-view text embeddings. Subsequently, we directly apply contrastive learning on these embeddings. Notably, our method eliminates the need for data augmentation operations to generate contrastive views while still leveraging the benefits of multi-view contrastive learning. Despite its simplicity, our model achieves outstanding performance, surpassing large language models on various datasets.

With the prosperity of SMS (short message service), there are more and more SMS spam. This paper proposes a method for filtering group sending SMS Spam, which combines black-and-white list technology and CAPTCHA (completely automated public Turing test to tell computers and human apart) technique. At first, it utilizes black-and-white list technique to check out the SMS account. If the SMS account is on the blacklist or white list, the SMS will be disposed directly without using CAPTCHA technique. In contrast, if the SMS account is not on the black-and-white list, then it makes use of CAPTCHA to handle it. If the SMS sender can not pass the CAPTCHA, the SMS will be tagged as s SMS spam and deleted. Finally, it has been simulated with Smartphone of Microsoft Visual Studio 2005.

Text classification is a task of assigning a set of text documents into predefined classes based on the classifier that learns from training samples; labelled or unlabeled. Binary text classifiers provide a way to separate related documents from a large dataset. However, the existing binary text classifiers are not grounded in reality due to the issue of overfitting. They try to find a clear boundary between relevant and irrelevant objects rather than understand the decision boundary. Normally, the decision boundary cannot be described as a clear boundary because of the numerous uncertainties in text documents. This paper attempts to address this issue by proposing an effective model based on sliding window technique (SW) and Support Vector Machine (SVM) to deal with the uncertain boundary and to improve the effectiveness of binary text classification. This model aims to set the decision boundary by dividing the training documents into three distinct regions (positive, boundary, and negative regions) to ensure the certainty of extracted knowledge to describe relevant information. The model then organizes training samples for the learning task to build a multiple SVMs based classifier. The experimental results using the standard dataset Reuters Corpus Volume 1 (RCV1) and TREC topics for text classification, show that the proposed model significantly outperforms six state-of-the-art baseline models in binary text classification.

Short Message Service (SMS) has evolved in the last few decades. The simplicity of SMS makes this short message service attractive to use as a direct communication service on mobile devices. As the popularity of this service increases, it also harms attacks on mobile devices such as SMS spam. Spam SMS are short messages that the recipient doesn't want, such as advertisements and scams. Spam SMS can overwhelm your inbox and make your mobile device experience less good. One way to overcome this problem is to implement a machine learning model to automatically recognize and filter Spam SMS. This research aims to build a machine learning model that provides higher accuracy for detecting SMS spam in Indonesian using the TF-IDF method and the Stochastic Gradient Descent Classifier. Based on the test results, the model built can detect SMS spam and not spam with an accuracy of 97%.

Short Message Service (SMS) is the most important communication tool in recent decades. With the increased popularity of mobile devices, the usage rate of SMS will increase more and more in years. SMS is a practical method used to reach individuals directly. But this practical and easy method can cause SMS to be misused. The advertising or promotional SMS of the companies are an examples of this misuse. In this study, a spam SMS detection technique is proposed using SVM. SMSSpamCollection dataset, which is contain 747 spam SMS and 4827 ham SMS, is used. 10 fold cross-validation technique is used to evaluate prediction of Spam SMS in the dataset. Therefore, proposed approach achieved 98.33 % true positive rate and 0,087 false positive rate for SVM classification algorithm.

Over the years, communication through Short Message Service (SMS) has been a primary tool for mobile subscribers. SMS has varied applications in health, industry, finances, education and social networking among others. The growth of mobile devices and SMS usage has consequently increased the attack surface for cyber-criminals culminating to the proliferation of malicious activities introduced using SMS spam, phishing, spyware, malware etc. Ham messages are normal messages people trade with one another and are usually not unwanted by the recipient, while spam messages are unsolicited junk and redundant messages that may be sent to a large number of people at once and are usually unwanted. Various spam detection models have been developed using various traditional machine and deep learning techniques. However, most studies where comparison between deep and traditional machine learning algorithms is done, have unfortunately omitted K-Nearest Neighbors and Support Vector Machine (SVM) which are empirically deemed as the most popular traditional machine learning algorithms. In this study, therefore, we develop a deep learning model based on Recurrent Neural Network (RNN) for Spam and Ham SMS classification and compare its performance against SVM model for the same University of California (UCl) SMS dataset. The results show that RNN has a slightly higher training and validation accuracy of 0.98 compared to SVM at 0.94, however, the false positive rate of SVM is marginally lower. Exploring application of deep learning with better optimization algorithms such as RNN improves accuracy, reduces computational complexities i.e. memory consumption and speed, and thus minimizing false positive rates. For future work, we suggest the use of varied performance metrics to validate the model in a distributed dataset environment.

Short Message Service (SMS) is one of the widely used mobile applications for global communication for personal and business purposes. Its widespread use for customer interaction, business updates, and reminders has made it a billion-dollar industry in “Text Marketing.” Along with valid SMS, a tsunami of spam messages also pop up that serve various purposes for the sender and the majority of them are fraudulent. Filtering spam SMS in an accurate manner is a crucial and challenging task that will benefit human lives both mentally and economically. Some of the challenges in the filtering of spam SMS include less number of characters, texts in informal languages, lack of public SMS spam corpus, and so on. Focusing solely on the textual features of the SMS is a major handicap of the existing methods, as it lacks in dynamically adapting to the increasing number of new keywords and jargon. In this article, we develop an intention-based approach of SMS spam filtering that efficiently handles dynamic keywords by focusing on the semantics of the words. We capture both semantic and textual features of the short-text messages based on 13 pre-defined intention labels. Moreover, the contextual embeddings of the texts are generated using various pre-trained NLP (Natural Language Processing) models. Finally, intention scores are computed for the pre-defined labels and a bunch of supervised learning classifiers are employed for filtering as spam or ham. Our approaches are evaluated on the SMS Spam Collection [ 24 ] benchmark dataset, and extensive experimentation shows interesting results. Our model did remarkably well with an accuracy of 98.07%, Precision and Recall of ∼ 0.97, which is better than few of the existing state-of-the-art alternatives. Though the accuracy of our approach is not the best among other existing approaches, the model is highly stable due to its emphasis on extracting the contextual features from the text through intention labels.

Short Message Service (SMS) is one of the mobile communication services that allows easy and inexpensive communication. Producing unwanted messages with the aim of advertising or harassment and sending these messages on SMS have become the biggest challenge in this service. Various methods have been presented to detect unsolicited short messages; many of which are based on machine learning. Neural Networks have been applied to separate the unwanted text messages (known as spam) from normal short messages (known as ham) in SMS. To the best of our knowledge, Recurrent Neural Network (RNN) has not been used in this issue yet. In this paper, we proposed a new method which utilizes RNN to separate the ham and spam with variable length sequences; even though we used a fixed sequence length. The proposed method achieved an accuracy of 98.11, indicates a considerable improvement compared to Support Vector Machine (SVM), token-based SVM and Bayesian algorithms with accuracies of 97.81, 97.64, and 80.54, respectively.

Contemporary spam filters increasingly rely on resource-intensive, deep learning models. This study evaluates the performance, robustness, and deployability of lightweight learning models. It provides a head-to-head evaluation of probabilistic (Naïve Bayes) and margin-based (Support Vector Machine) classifiers on three feature spaces derived from the 5574-message UCI Short Message Service (SMS) spam collection. Our primary finding shows that a hybrid model, a fusion of bag-of-words (BoW) representation with 22 handcrafted metadata features, achieves the highest accuracy, with SVM peaking at 98.3%. To assess the resilience, the model was tested against adversarial attacks. The hybrid SVM model exhibited strong robustness when faced with altered data and maintained 72.41% accuracy against challenging semantic attacks. Furthermore, the hybrid SVM model demonstrated strong cross-dataset generalization, achieving 74.38% accuracy when trained on the original UCI data and tested on a modern, diverse dataset of SMS, Telegram, and email messages. Deployment analysis confirmed the efficiency of the framework, with processing of ~ 200 requests/s (fastest model) at less than 10ms latency and ~ 1.5% average CPU load on a standard CPU. The results establish three key principles for next-generation spam filters: (i) lexical information remains the dominant signal; (ii) lightweight metadata provides measurable incremental value when paired with text; and (iii) margin-based classifiers exploit multimodal fusion most effectively. Taken together, these findings validate that a lightweight hybrid feature-engineering approach provides a robust, generalizable, and resource-efficient solution for real-time spam mitigation, thereby presenting a compelling and practical alternative to computationally expensive deep learning architectures.

Short Message Service (SMS) spam is increasingly becoming a problem for many telecommunication service providers. Not only do SMS spam messages use mobile network resources abusively, but also in many cases they represent malware propagation vectors for mobile devices. In this work, we propose a network-based online detection method for SMS spam messages. The proposed scheme uses robust text signatures to identify similar messages that are sent excessively in the SMS platform and is robust against slight modifications in SMS spam messages. Additionally, the method uses a fast online algorithm which can be deployed in large carrier networks to detect spam activities before too many spam messages are delivered. It does not store SMS message contents, therefore it does not compromise the privacy of mobile subscribers.

With the overflowing of Short Message Service (SMS) spam nowadays, many traditional text classification algorithms are used for SMS spam filtering. Nevertheless, because the content of SMS spam messages are miscellaneous and distinct from general text files, such as more shorter, usually including mass of abbreviations, symbols, variant words and distort or deform sentences, the traditional classifiers aren't fit for the task of SMS spam filtering. In this paper, the authors propose a Short Message Biterm Topic Model (SM-BTM) which can be used to automatically learn latent semantic features from SMS spam corpus for the task of SMS spam filtering. The SM-BTM is based on the probability of topic model theory and Biterm Topic Model (BTM). The experiments in this work show the proposed model SM-BTM can acquire higher quality of topic features than the original BTM, and is more suitable for identifying the miscellaneous SMS spam.

In the modern life, SMS (Short Message Service) is one of the most necessary services on mobile devices. Because of its popularity, many companies use SMS as an effective marketing and advertising tool. Also, the popularity gives hackers chances to abuse SMS to cheat mobile users and steal personal information in their mobile phones, for example. In this paper, we propose a method to detect spam SMS on mobile devices and smart phones. Our approach is based on improving a graph-based algorithm and utilizing the KNN Algorithm - one of the simplest and most effective classification algorithms. The experimentation is carried out on SMS message collections and the results ensures the efficiency of the proposed method, with high accuracy and small processing time enough for detecting spam messages directly on mobile phones in real time.

Over the past few years, short message service (SMS) usage has significantly increased. This service is used to deliver text messages by billions of people. Service providers have launched a number of popular applications, including mobile banking, summons checkpoints, SMS chat, and others. This chapter explores the numerous SMS applications that are available to users and provides an outline of how this service is provided. We examine the causes of its success and the problems that need to be solved. We also look at upcoming trends and the difficulties that to improve this service, certain obstacles must be overcome. This chapter should help you understand how SMS applications work and what to expect from them going forwards given the improvements to current SMS and technological advancement. We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340 ms in the case where the dictionary size of Bob’s model includes all words (n 5200) and Alice’s SMS has at most m 160 unigrams. In the case with n 369 and m 8 (the average of a spam SMS in the database), our solution takes only 21 ms.

Semi-supervised learning using frequent itemset and ensemble learning for SMS classification

TruSMS: A trustworthy SMS spam control system based on trust management