# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 2 Related Work  
Researchers have proposed many methods to identify SMS spam in recent years. In these studies, researchers used data mining and machine learning techniques. Some researchers, on the other hand, recommended the Bayes algorithm in order to classify messages as spam (unsolicited) or non-spam (solicited). Classifiers such as Decision Tree, Support Vector Machine, Random Forest, AdaBoost, and even a rule-based classification technique have been used by researchers. In some studies, researchers were interested in reorganizing the structure of the messages. In addition, researchers have started to use deep learning techniques in order to detect unsolicited text messages in recent times. There are only a few studies dealing with the problem of detecting unsolicited text messages in different languages. In one of these studies [24], the researchers proposed a mobile-based approach to identify SMS spam messages. The approach is based on the implementation of Bayesian learning and SVM methods. At the end of the study, a novel Unsolicited SMS dataset was presented for experimental evaluation. The researchers in [25] proposed an analysis of various methods to identify spam messages on a dataset of Indian messages. Four different methods, Support Vector Machine, Random Forest, AdaBoost, and Multinomial Naive Bayes, were tested in this study. In another study, the authors proposed a novel model aiming to enhance the classification performance regarding SMS spam. In this model, short and messy text messages were expanded through normalization. This enabled semantic analysis and context detection to be performed. The authors revealed that the proposed model carried out SMS spam detection with high performance. In a different study, the model proposed by the researchers was designed to cooperate with messages in detecting unsolicited text messages. A phase was introduced to expand these messages. This approach was based on natural language processing. The aim was to align the size of the words in order to expand the data [26]. In another study, a Genetic Algorithm-Trained Bayesian Network model was proposed by the authors. In the proposed model, noisy features were normalized and the text was expanded. The Bayesian algorithm was used as a classifier in the model, using a genetic algorithm for feature selection. It was revealed that the proposed model operated with high accuracy [27]. In [28], a novel model based on contextualized word representation was proposed with the aim of identifying spam messages on social networks. In this model, a Contextualized Bi-directional LSTM network was developed through embedding from language models. It was revealed by the authors that the results obtained at the end of the experimental study outperformed the existing methods. In [29], a novel model was proposed by the authors with the aim of detecting spam comments on social networks. Spam comments were detected through a certain custom heuristic method such as n-gram, differently from standard machine learning and heuristic methods. The proposed model was tested on YouTube, X, and SMS spam. It was revealed by the results obtained that the highest performance was attained in SMS spam detection. In another study, [30], the researchers recommended a model based on two data mining methods, namely, False Positive-Growth (FPG) and Naive Bayes (NB) in filtering unsolicited text messages. The FPG technique was used in order to extract frequent items in messages. The NB algorithm, on the other hand, was used to identify messages as unsolicited or solicited. The conducted tests revealed that an average accuracy of 98.5% was attained. In another study, the Orange 3 agent was used with the aim of achieving the best algorithm in terms of classification and identification of SMS spam messages. The assessment results revealed that accuracy and error rates were different for various algorithms.