Angello Dushantha, Rukshan Akalanka, Hemantha Gayan, Kasun C. Siriwardhana, P. Haddela, L. Wickramasinghe
{"title":"Evaluation Method for Video Advertisetments Using EEG Signals","authors":"Angello Dushantha, Rukshan Akalanka, Hemantha Gayan, Kasun C. Siriwardhana, P. Haddela, L. Wickramasinghe","doi":"10.1109/icac51239.2020.9357234","DOIUrl":null,"url":null,"abstract":"Video advertisements became very popular in recent past due to the technology advancement and competitiveness of businesses. Therefore, analyzing the impact of commercial video advertisements are important before they launch the marketing campaign. This paper presents a unique method that can evaluate the effectiveness of movie commercials (trailers) using Electroencephalogram (EEG) signals captured from a brain computer interface. Randomly selected fifteen movie lovers were participated to capture EEG signals. For a selected set of movie trailers, three different type of classification models were trained and tested. With the help of classification models, it measures attention and enjoyment levels and also emotion status of a viewer to compute effectiveness of an advertisement. It also consists of a recommender system which suggests movie advertisements based on the preferences of the users. From the initial results received, it confirms that proposed framework is producing promising results. Even though this work focuses on the movie/entertainment industry, it has the potential to be developed and applied for many other industries as well.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 2nd International Conference on Advancements in Computing (ICAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icac51239.2020.9357234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Video advertisements became very popular in recent past due to the technology advancement and competitiveness of businesses. Therefore, analyzing the impact of commercial video advertisements are important before they launch the marketing campaign. This paper presents a unique method that can evaluate the effectiveness of movie commercials (trailers) using Electroencephalogram (EEG) signals captured from a brain computer interface. Randomly selected fifteen movie lovers were participated to capture EEG signals. For a selected set of movie trailers, three different type of classification models were trained and tested. With the help of classification models, it measures attention and enjoyment levels and also emotion status of a viewer to compute effectiveness of an advertisement. It also consists of a recommender system which suggests movie advertisements based on the preferences of the users. From the initial results received, it confirms that proposed framework is producing promising results. Even though this work focuses on the movie/entertainment industry, it has the potential to be developed and applied for many other industries as well.