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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5589
Title: Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction
Authors: Kumar S.
Yadava M.
Pratim Roy, Partha
Published in: Information Fusion
Abstract: 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. © 2018 Elsevier B.V.
Citation: Information Fusion (2019), 52(): 41-52
URI: https://doi.org/10.1016/j.inffus.2018.11.001
http://repository.iitr.ac.in/handle/123456789/5589
Issue Date: 2019
Publisher: Elsevier B.V.
Keywords: ABC
EEG
Multimodal
Neuroscience
Rating prediction
Sentiment analysis
ISSN: 15662535
Author Scopus IDs: 57197068495
57193672890
56880478500
Author Affiliations: Kumar, S., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India
Yadava, M., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India
Corresponding Author: Kumar, S.; Department of Computer Science and Engineering, Indian Institute of TechnologyIndia; email: skumar2@cs.iitr.ac.in
Appears in Collections:Journal Publications [CS]

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