Multilingual Spam Classification Using Advanced Deep Learning Techniques

The increasing importance of SMS spam has made it crucial to detect spam messages in several languages in order to provide accurate information. In this paper, several deep learning techniques, such as recurrent neural networks (RNN), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent units (GRU), are used for multilingual spam identification. The text in the dataset were classified as “spam” or “ham”. The dataset consists of spam and ham messages in four languages English, Hindi, French and German. The text data is preprocessed using tokenization, padding to standardize input length, and label encoding to improve model performance. Next, using the deep learning models, spam messages are identified throughout the multilingual dataset. Additionally, the models were applied by combining all text messages from the four languages into a single column for joint analysis. Each algorithm is evaluated based on its ability to capture linguistic patterns in the text and accurately differentiate between spam and non-spam reviews. The deep learning techniques also applied by combining all text messages of four languages. The outcomes highlighted the deep learning model's capacity to identify long-term dependencies in the text data for multilingual spam detection. This work contributes to advancing spam detection techniques across languages using deep learning.