Abstract
With the advancement in computing hardware and cloud services, many applications, imagined in the past and rejected as complicated or unfeasible, are becoming achievable. Artificial intelligence was able via new computing architecture to overcome big time consuming and insufficient data storage which permitted rapid models development and real-time applications. In this paper, we show via a challenging application how artificial intelligence can change the way we interact with our devices. Particularly, the paper attempts to develop an emotion-based search engine. In this sense, the user emotional features are used to select best Internet search results or to adapt them to user emotion. In this case, many scenarios are possible such as preventing bad influence of the search results on the user emotion. The idea presented in this solution can be adapted to other applications and brings new research challenges.
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References
Burkert, P., Trier, F., Afzal, M.Z., Dengel, A., Liwicki, M.: Dexpression: deep convolutional neural network for expression recognition. CoRR, abs/1509.05371 (2015)
Duncan, D.L., Shine, G., English, C.: Facial emotion recognition in real time (2016)
Hong, J., Fang, M.: Sentiment analysis with deeply learned distributed representations of variable length texts (2015)
Kanger, N., Bathla, G.: Recognizing emotion in text using neural network and fuzzy logic. Indian J. Sci. Technol. 10(12), 1–6 (2017)
Katz, P., Singleton, M., Wicentowski, R.: SWAT-MP: the SemEval-2007 systems for task 5 and task 14. In: Proceedings of the 4th International Workshop on Semantic Evaluations, SemEval 2007, Stroudsburg, PA, USA, pp. 308–313. Association for Computational Linguistics (2007)
Qamar, S., Ahmad, P.: Emotion detection from text using fuzzy logic. Int. J. Comput. Appl. 121, 29–32 (2015)
Saragih, J.M., Lucey, S., Cohn, J.F.: Deformable model fitting by regularized landmark mean-shift. Int. J. Comput. Vision 91(2), 200–215 (2011)
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Benazzouz, Y., Boudour, R. (2020). An Emotion-Based Search Engine. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1069. Springer, Cham. https://doi.org/10.1007/978-3-030-32520-6_15
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DOI: https://doi.org/10.1007/978-3-030-32520-6_15
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