Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction

This paper proposes a novel multimodal framework for rating prediction of consumer products by fusing different data sources, namely physiological signals, global reviews obtained separately for the product and its brand. The reviews posted by global viewers are retrieved and processed using Natural Language Processing (NLP) technique to compute compound score considered as global rating. Also, electroencephalogram (EEG) signals of the participants were recorded simultaneously while watching different products on computer's screen. From EEG, valence scores in terms of product rating are obtained using self-report towards each viewed product for acquiring local rating. A higher valence score corresponds to intrinsic attractiveness of the participant towards a product. Random forest based regression techniques is used to model EEG data to build a rating prediction framework considered as local rating. Furthermore, Artificial Bee Colony (ABC) based optimization algorithm is used to boost the overall performance of the framework by fusing global and local ratings. EEG dataset of 40 participants including 25 male and 15 female is recorded while viewing 42 different products available on e-commerce website. Experiment results are encouraging and suggest that the proposed ABC optimization approach can achieve lower Root Mean Square Error (RMSE) in rating prediction as compared to individual unimodal schemes.