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- W4286593235 abstract "Sentiment analysis works on the principle of categorizing and identifying the text-based content and the process of classifying documents into one of the predefined classes commonly known as text classification. Hackers deploy a strategy by sending malicious content as an advertisement link and attack the user system to gain information. For protecting the system from this type of phishing attack, one needs to classify the spam data. This chapter is based on a discussion and comparison of various classification models that are used for phishing SMS detection through sentiment analysis. In this chapter, SMS data is collected from Kaggle, which is classified as ham or spam; while implementing the deep learning techniques like Convolutional Neural Network (CNN), CNN with 7 layers, and CNN with 11 layers, different results are generated. For evaluating these results, different machine learning techniques are used as a baseline algorithm like Naive Bayes, Decision Trees, Support Vector Machine (SVM), and Artificial Neural Network (ANN). After evaluation, CNN showed the highest accuracy of 99.47% as a classification model." @default.
- W4286593235 created "2022-07-22" @default.
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- W4286593235 date "2022-06-10" @default.
- W4286593235 modified "2023-09-29" @default.
- W4286593235 title "Deep Learning Based Sentiment Analysis for Phishing SMS Detection" @default.
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- W4286593235 doi "https://doi.org/10.4018/978-1-6684-6303-1.ch046" @default.
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