Abstract
The idea of applying a conjunction of sentiment and social network analysis to improve the performance of applications has recently attracted attention of researchers. In widely used online shopping websites, customers can provide reviews about a product. Also a number of relations like friendship, trust and similarity between products or users are being formed. In this paper a combination of sentiment analysis and social network analysis is employed for extracting classification rules for each customer. These rules represent customers’ preferences for each cluster of products and can be seen as a user model. The combination helps the system to classify products based on customers’ interests. We compared the results of our proposed method with a baseline method with no social network analysis. The experiments on Amazon’s meta-data collection show improvements in the performance of the classification rules compared to the baseline method.
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References
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Shams, M., Saffar, M., Shakery, A., Faili, H. (2012). Applying Sentiment and Social Network Analysis in User Modeling. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28604-9_43
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DOI: https://doi.org/10.1007/978-3-642-28604-9_43
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