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Neural Netw. 2017 Feb 16. pii: S0893-6080(17)30034-5. doi: 10.1016/j.neunet.2017.01.013. [Epub ahead of print]

Prediction of advertisement preference by fusing EEG response and sentiment analysis.

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Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India. Electronic address:
Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India.
School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India.


This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data.


EEG signal analysis; Multimedia indexing and retrieval; Multimodal rating; Predictive modeling; Sentiment analysis

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