A COMPARISON OF MACHINE LEARNING TECHNIQUES: E-MAIL SPAM FILTERING FROM COMBINED SWAHILI AND ENGLISH EMAIL MESSAGES

The speed of technology change is faster now compared to the past ten to fifteen years. It changes the way people live and force them to use the latest devices to match with the speed. In communication perspectives nowadays, use of electronic mail (e-mail) for people who want to communicate with friends, companies or even the universities cannot be avoided. This makes it to be the most targeted by the spammer and hackers and other bad people who want to get the benefit by sending spam emails. The report shows that the amount of emails sent through the internet in a day can be more than 10 billion among these 45% are spams. The amount is not constant as sometimes it goes higher than what is noted here. This indicates clearly the magnitude of the problem and calls for the need for more efforts to be applied to reduce this amount and also minimize the effects from the spam messages. Various measures have been taken to eliminate this problem. Once people used social methods, that is legislative means of control and now they are using technological methods which are more effective and timely in catching spams as these work by analyzing the messages content. In this paper we compare the performance of machine learning algorithms by doing the experiment for testing English language dataset, Swahili language dataset individual and combined two dataset to form one, and results from combined dataset compared them with the Gmail classifier. The classifiers which the researcher used are Naïve Bayes (NB), Sequential Minimal Optimization (SMO) and k-Nearest Neighbour (k-NN). The results for combined dataset shows that SMO classifier lead the others by achieve 98.60% of accuracy, followed by k-NN classifier which has 97.20% accuracy, and Naïve Bayes classifier has 92.89% accuracy. From this result the researcher concludes that SMO classifier can work better in dataset that combined English and Swahili languages. In English dataset shows that SMO classifier leads other algorism, it achieved 97.51% of accuracy, followed by k-NN with average accuracy of 93.52% and the last but also good accuracy is Naïve Bayes that come with 87.78%. Swahili dataset Naïve Bayes lead others by getting 99.12% accuracy followed by SMO which has 98.69% and the last was k-NN which has 98.47%

Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection

This paper investigates the effectiveness of large language models (LLMs) in email spam detection by comparing prominent models from three distinct families: BERT-like, Sentence Transformers, and Seq2Seq. Additionally, we examine well-established machine learning techniques for spam detection, such as Na\"ive Bayes and LightGBM, as baseline methods. We assess the performance of these models across four public datasets, utilizing different numbers of training samples (full training set and few-shot settings). Our findings reveal that, in the majority of cases, LLMs surpass the performance of the popular baseline techniques, particularly in few-shot scenarios. This adaptability renders LLMs uniquely suited to spam detection tasks, where labeled samples are limited in number and models require frequent updates. Additionally, we introduce Spam-T5, a Flan-T5 model that has been specifically adapted and fine-tuned for the purpose of detecting email spam. Our results demonstrate that Spam-T5 surpasses baseline models and other LLMs in the majority of scenarios, particularly when there are a limited number of training samples available. Our code is publicly available at https://github.com/jpmorganchase/emailspamdetection

Text Preprocessing using Annotated Suffix Tree with Matching Keyphrase

Text document is an important source of information and knowledge. Most of the knowledge needed in various domains for different purposes is in form of implicit content. Content of text is represented by keyphrases, which consist of one or more meaningful words. Keyphrases can be extracted from text through several steps of processing, including text preprocessing. Annotated Suffix Tree (AST) built from the documents collection itself is used to extract the keyphrase, after basic text preprocessing that includes removing stop words and stemming are applied. Combination of four variations of preprocessing is used. Two words (bi-words) and three words of phrases extracted are used as a list of keyphrases candidate which can help user who needs keyphrase information to understand content of documents. The candidate of keyphrase can be processed further by learning process to determine keyphrase or non keyphrase for the text domain with manual validation. Experiments using simulation corpus which keyphrases are determined from it show that keyphrases of two and three words can be extracted more than 90% and using real corpus of economy, keyphrases or meaning phrases can be extracted about 70%.   The proposed method can be an effective ways to find candidate keyphrases from collection of text documents which can reduce non keyphrases or non meaning phrases from list of keyphrases candidate and detect keyphrases which are separated by stop words

A pipeline and comparative study of 12 machine learning models for text classification

Text-based communication is highly favoured as a communication method, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam emails, to deceive users into relaying personal information, including online accounts credentials or banking details. For this reason, many machine learning methods for text classification have been proposed and incorporated into the services of most email providers. However, optimising text classification algorithms and finding the right tradeoff on their aggressiveness is still a major research problem. We present an updated survey of 12 machine learning text classifiers applied to a public spam corpus. A new pipeline is proposed to optimise hyperparameter selection and improve the models' performance by applying specific methods (based on natural language processing) in the preprocessing stage. Our study aims to provide a new methodology to investigate and optimise the effect of different feature sizes and hyperparameters in machine learning classifiers that are widely used in text classification problems. The classifiers are tested and evaluated on different metrics including F-score (accuracy), precision, recall, and run time. By analysing all these aspects, we show how the proposed pipeline can be used to achieve a good accuracy towards spam filtering on the Enron dataset, a widely used public email corpus. Statistical tests and explainability techniques are applied to provide a robust analysis of the proposed pipeline and interpret the classification outcomes of the 12 machine learning models, also identifying words that drive the classification results. Our analysis shows that it is possible to identify an effective machine learning model to classify the Enron dataset with an F-score of 94%.Comment: This article has been accepted for publication in Expert Systems with Applications, April 2022. Published by Elsevier. All data, models, and code used in this work are available on GitHub at https://github.com/Angione-Lab/12-machine-learning-models-for-text-classificatio