# SMS Spam Detection System Based on Deep Learning Architectures for Turkish and English Messages
## 2 Related Work
This study demonstrated that the neural network and the Naive Bayes algorithm had great accuracy on the TurkishSMS dataset and the UCI SMS Spam dataset, respectively, and a lower missing rate based on the accuracy and error rates. It was observed that the text language, which is the main difference between the datasets, had an impact on the algorithm results. Various results were obtained in terms of accuracy when the SVM algorithm was used on the TurkishSMS and UCI SMS Spam datasets. However, it was also observed that it was the algorithm attaining the minimum missing rate for both datasets [31]. In [32], the authors recommended using deep learning architectures for binary classification. The model proposed in the study uses the deep learning methods of LSTM and CNN together. In order to evaluate the performance of this model, a comparison was performed with different machine learning algorithms. In another study, different DL and ML algorithms were trained on the TurkishSMS dataset. It was revealed that CNN appeared as the most successful algorithm, with an accuracy value of 99.86% [33]. In another study, the impact of preprocessing and the feature selection method on filtering SMS spam, particularly for the Turkish language, was inclusively investigated by the authors in terms of classification accuracy. Only the bag-of-words approach was used while creating the feature vector. The effectiveness of the feature selection strategies used in the study, on the other hand, was tested on two different classifiers. The outcome of this study can be an indicator for languages with similar features as the Turkish language is the leading example of agglutinative languages [34]. In another study, the authors proposed a hybrid approach using CNN and LSTM algorithms. The Tiago dataset was preferred in the study, using BUNOW and GloVe as word embedding techniques. It was determined that the model displayed an accuracy value of 98.44% [35]. In another study, using static and dynamic word embedding techniques, a multi-channel CNN model was proposed for the classification of SMS spam. It was stated by the researchers that the proposed approach attained an accuracy rate of 96.12% [36]. A BiLSTM-based spam detection model was developed by [PERSON] et al. The UCI SMS and ExAIS_SMS datasets were used in this study. The proposed model attained a 93.4% accuracy on the UCI SMS dataset and 98.6% on the ExAIS_SMS dataset [37]. [PERSON] et al. tested various ML and DL models for SMS spam detection in their study. It was explained that Logistic Regression and Random Forest methods attained the highest accuracy values [38]. In another study, a hybrid classifier based on SMS spam classification and sentiment analysis was proposed. In this study, feature extraction was conducted by using Word2 Vec. The features were determined according to six various feature selection methods. In the proposed model, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) were used. In addition, the Rat Swarm Optimization (RSO) algorithm was used in order to adjust the parameters of the network. Finally, AFINN and SentiWordNet were used for sentiment analysis. The proposed model was evaluated on three datasets. According to the obtained results, the highest performance rate was determined as 99.82% [39]. In another study, the authors attempted to detect spam mail by using the sentiment analysis-based semantic FE and hybrid FS techniques. In this study, a sentiment analysis procedure was carried out through semantic feature extraction and hybrid FS. In the sentiment analysis, on the other hand, the e-mail spam classification was conducted by analyzing the input texts. The authors used TF-IDF, Information Gain, and the Gini Index for feature extraction. The experimental results showed that the Gini Index FS technique attained a higher performance, of 95.17%, and RF with the Gini Index and Word2 Vec methods had an accuracy rate of 93.3% in spam e-mail detection [40]. In another study, the authors used CNN and GRU algorithms with the aim of spam e-mail detection.