Abstract
The online reviews not only have huge impact on consumer shopping behavior but also online stores’ marketing strategy. Positive reviews will have positive influence for consumer’s buying decision. Therefore, some sellers want to boost their sales volume. They will hire spammers to write undeserving positive reviews to promote their products. Currently, some of the researches related to detection of fake reviews based on the text feature, the model will reach to high accuracy. However, the same model test on the other dataset the accuracy decrease sharply. Relevant researches had gradually explored the identification of fake reviews across different domains, whether the model built using comprehensive methods such as text features or neural networks, encountering the decreasing of accuracy. On the other hand, the method didn’t explain why the model can be applied to cross-domain predictions. In our research, we using the fake reviews and truthful reviews from Ott et al. (2011) and Li, Ott, Cardie, and Hovy (2014) in the three domain (hotel, restaurant, doctor). The cross-domain detect model based on Stimuli Organism Response (S-O-R) combine LIWC (Linguistic Inquiry and Word Count), add word2vec quantization feature, overcoming the decreasing accuracy situation. According to the research result, in the method one SOR calculation of feature weight of reviews, the DNN classification algorithm accuracy is 63.6%. In the method two, calculation of frequent features of word vectors, the random forest classification algorithm accuracy is 73.75%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adelaar, T., Chang, S., Lancendorfer, K.M., Lee, B., Morimoto, M.: Effects of media formats on emotions and impulse buying intent. J. Inf. Technol. 18(4), 247–266 (2003). https://doi.org/10.1080/0268396032000150799
Aslam, U., Jayabalan, M., Ilyas, H., Suhail, A.: A survey on opinion spam detection methods. Int. J. Sci. Technol. Res. 8(9) (2019)
Boujbel, L., d’Astous, A.: Exploring the feelings and thoughts that accompany the experience of consumption desires. Psychol. Mark. 32(2), 219–231 (2015). https://doi.org/10.1002/mar.20774
Chang, H.-J., Eckman, M., Yan, R.-N.: Application of the Stimulus-Organism-Response model to the retail environment: the role of hedonic motivation in impulse buying behavior. Int. Rev. Retail Distrib. Consum. Res. 21(3), 233–249 (2011)
Eroglu, S.A., Machleit, K.A., Davis, L.M.: Atmospheric qualities of online retailing: a conceptual model and implications. J. Bus. Res. 54(2), 177–184 (2001)
Gatautis, R., Vaiciukynaite, E.: Website atmosphere: towards revisited taxonomy of website elements. Econ. Manag. 18(3) (2013)
Kavanagh, D.J., Andrade, J., May, J.: Imaginary relish and exquisite torture: the elaborated intrusion theory of desire. Psychol. Rev. 112(2), 446 (2005)
Klaus, T., Changchit, C.: Toward an understanding of consumer attitudes on online review usage. J. Comput. Inf. Syst. 59(3), 277–286 (2017). https://doi.org/10.1080/08874417.2017.1348916
Li, J., Ott, M., Cardie, C., Hovy, E.: Towards a general rule for identifying deceptive opinion spam. Paper Presented at the Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2014)
Liu, W., Jing, W., Li, Y.: Incorporating feature representation into BiLSTM for deceptive review detection. Computing 102(3), 701–715 (2019). https://doi.org/10.1007/s00607-019-00763-y
Menon, S., Kahn, B.: Cross-category effects of induced arousal and pleasure on the Internet shopping experience. J. Retail. 78(1), 31–40 (2002)
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. Paper Presented at the Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (2011)
Ren, Y., Ji, D.: Neural networks for deceptive opinion spam detection: an empirical study. Inf. Sci. 385–386, 213–224 (2017). https://doi.org/10.1016/j.ins.2017.01.015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wei, CS., Hsu, PY., Huang, CW., Cheng, MS., Prassida, G.F. (2020). Devising a Cross-Domain Model to Detect Fake Review Comments. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_58
Download citation
DOI: https://doi.org/10.1007/978-3-030-63119-2_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63118-5
Online ISBN: 978-3-030-63119-2
eBook Packages: Computer ScienceComputer Science (R0)