USING MACHINE LEARNING MODELS TO INVESTIGATE CONSUMER ATTITUDES TOWARD ONLINE BEHAVIORAL ADVERTISING
USING MACHINE LEARNING MODELS TO INVESTIGATE CONSUMER ATTITUDES TOWARD ONLINE BEHAVIORAL ADVERTISING

Summary/Abstract: The technique of online behavioral advertising (OBA) is a strategy that has been widely used in the last decade by businesses and advertisers to deliver targeted advertising messages to internet users. It is done by utilizing technology to record the habits of online shoppers, including their searches and the content they visit. Users who browse the internet or use social media view advertisements relevant to their interests, recent searches, and location. We study Twitter users’ attitudes about targeted ads using five different machine learning models in this research, applying the CRISP-DM framework. Our primary focus is to develop a benchmark Twitter sentiment dataset related to targeted ads and implement highly accurate machine learning algorithms to predict tweet text sentiments when discussing targeted ads. The machine learning algorithms used are Logistic Regression, Random Forest, Multinomial Naïve Bayes, Multi-Layer Perceptron, and Decision Tree. We use accuracy, precision, recall, and the F1 measure to evaluate their performance. Logistic Regression using the content-based method provides the utmost accuracy of 0.88. We propose a model that allows real-time consumer attitude research regarding retargeting ads. The results show that logistic regression is the most accurate method for predicting customer responses to OBA campaigns and that retargeting and OBA often cause negative feelings in consumers.

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