Abstract
Spam text messages are unwanted messages sent to a large number of users on their mobile phones by telemarketers, companies to advertise their products and services and can often be a trap set by a scammer. These junk messages are capable of installing malware on phones if the user engages with the messages and can also be an attempt to steal the private information of the user. Thus, it is necessary to classify and detect these messages in order to protect the user from being a victim of such traps and to prevent identity theft of the user. Spam text messages can be detected by creating a corpus of text message words and identifying or classifying the words common to the spam text messages. In order to create a corpus, the data first has to be cleaned. Feature extraction can be performed on the cleaned data using various methods like term frequency, TF-IDF, Word2Vec, and GloVe. In this paper, we have classified the spam text from the messages by applying Naïve Bayes Classifier, Logistic Regression, LSTM and Convolutional Neural Network (CNN ). Further, we have presented their comparative analysis by calculating accuracy, precision, recall and F1 score.
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Mundra, S., Mundra, A., Saigal, A., Gupta, P., Agarwal, J., Goyal, M.K. (2022). Machine Learning Approaches for the Classification of Spammed Text in Messages. In: Somani, A.K., Mundra, A., Doss, R., Bhattacharya, S. (eds) Smart Systems: Innovations in Computing. Smart Innovation, Systems and Technologies, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2877-1_56
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