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Integrating contextual sentiment analysis in collaborative recommender systems

Author

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  • Nurul Aida Osman
  • Shahrul Azman Mohd Noah
  • Mohammad Darwich
  • Masnizah Mohd

Abstract

Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers’ feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.

Suggested Citation

  • Nurul Aida Osman & Shahrul Azman Mohd Noah & Mohammad Darwich & Masnizah Mohd, 2021. "Integrating contextual sentiment analysis in collaborative recommender systems," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-21, March.
  • Handle: RePEc:plo:pone00:0248695
    DOI: 10.1371/journal.pone.0248695
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    References listed on IDEAS

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    1. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
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    1. Arodh Lal Karn & Rakshha Kumari Karna & Bhavana Raj Kondamudi & Girish Bagale & Denis A. Pustokhin & Irina V. Pustokhina & Sudhakar Sengan, 2023. "RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis," Electronic Commerce Research, Springer, vol. 23(1), pages 279-314, March.

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