Predicting effectiveness of marketing through analyzing emotional context in advertisement using deep learning

Abstract

In this modern age, marketing strategy is becoming a new challenge. Not only the global market but also people’s choices are shifting to catch the attention of buyers. Also, based on consumer’s choice organizations are bringing changes in their marketing policy to increase the chances of their product selling rate. Basically, to promote their products and grab buyer’s attention they are promoting advertisements on every media platform. But they are not aware of the effectiveness of marketing and which emotional states are needed more and which are not needed much. Therefore, we lead this study to recognize a successful advertisement and identify the rate of the emotional states which make good impact in people mind to purchase the product. Using deep learning and supervised machine learning algorithms as well as feature extraction methods for instance, LSTM-RNN, SVM, XGBOOST, Na¨ıve Bayes, Multiple Linear Regression, MFCC, Zero-Crossing Rate, Power Spectral Density, we find out and evaluate the rate of the emotional states to figure out the liking and purchase intent which makes an advertisement successful.