The Use of Sentiment Analysis and Machine Learning Methods for Spam Detection in Twitter

The welcoming of social networks, especially Twitter, has posed a new challenge to researchers, and it is nothing but spam. Numerous different approaches to deal with spam are presented. In this study, we attempt to enhance the accuracy of spam detection by applying one of the latest spam detection techniques and its combination with sentiment analysis. Using the word embedding technique, we give the tweet text as input to a convolutional neural network (CNN) architecture, and the output is the label of the tweet as spam or normal. Simultaneously, by extracting the suitable features in the Twitter network and applying machine learning methods, in a separate procedure, the Tweeter spam detection is done. Eventually, the output of both approaches are used as inputs to an ensemble convolutional neural network so that its output specifies the final decision as normal or spam. In this study, we employ both balanced and unbalanced datasets to examine the impact of the proposed model on two types of data. The results indicate an increase in the accuracy of the proposed method in both datasets.

Keywords:

Spam Detection , Twitter , Word Embedding , Convolutional neural network , Deep learning