Need of Sentiments Analysis with CF for Quality Recommendations
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Submitted Jan 8, 2021
Published Mar 20, 2021
Published Mar 20, 2021
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Recommendation system (RS) help user for purchasing the right product of their interest within the affordable right price. Presently many RS make use of only filtering methods to recommend products to the user which is not taking care of the quality of products. Quality of products can be found from textual reviews available on various e-commerce websites and hence this RS performs Sentiment Analysis (SA)of extracted relevant textual reviews along with Collaborative Filtering (CF) to give accurate and good quality recommendations to the user. Reviews are analyzed using optimized Artificial Neural Network (ANN) which shows notified improvement than traditional ANN on real-time extracted data of reviews.CF performance is proved by using the standard dataset of movilense used in many research papers. Results show high recall and accuracy of CF for the recommendation of products to the target user.
Keywords: clustering; Recommendation systems; Collaborative Filtering; Artificial Neural Network
References
S. Sun, C. Luo, and J. Chen, “A review of natural language processing techniques for opinion mining systems,” Information Fusion, vol. 36, pp. 10–25, 2017. [Online]. Available: 10.1016/j.inffus.2016.10.004; https://dx.doi.org/10.1016/j.inffus.2016.10.004.
E. Bilici and Y. Sayg?n, “Why do people (not) like me?: Mining opinion influencing factors from reviews,” Expert Systems with Applications, vol. 68, pp. 185–195, 2017. [Online]. Available: 10.1016/j.eswa.2016. 0.001; https://dx.doi.org/10.1016/j.eswa. 2016.10.001.
E. Marrese-Taylor, J. D. Velásquez, and F. Bravo-Marquez, “A novel deterministic approach for aspect-based opinion mining in tourism products reviews,” Expert Systems with Applications, vol. 41, no. 17, pp. 7764–7775, 2014. [Online]. Available: 10.1016/j.eswa.2014.05.045;https://dx.doi.org/10.1016/j.eswa.2014.05.045.
K. Ravi and V. Ravi, “A survey on opinion mining and sentiment analysis: Tasks, approaches and applications,” pp. 14–46, 2015. [Online]. Available: 10.1016/j.knosys.2015.06.015; https://dx.doi.org/10. 1016/j.knosys.2015.06.01.
R. Dehkharghani, H. Mercan, A. Javeed, and Y. Saygin, “Sentimental causal rule discovery from Twitter,” Expert Systems with Applications, vol. 41, no. 10, pp. 4950–4958, 2014. [Online]. Available: 10.1016/j. eswa.2014.02.024; https://dx.doi.org/10.1016/j.eswa.2014.02.024.
M. J. Pazzani and D. Billsus, Content-based RSs. Berlin, Heidelberg: Springer, 2007.
F. Zhang, “A Personalized Time-Sequence-Based Book Recommendation Algorithm for Digital Libraries,” IEEE Access, vol. 4, pp. 2714–2720, 2016. [Online]. Available: 10.1109/access.2016.2564997; https: //dx.doi.org/10.1109/ access.2016. 2564997.
X. Wu, B. Cheng, and J. Chen, “Collaborative filtering service recommendation based on a novel similarity computation method,” IEEE Transactions on Services Computing, vol. 10, pp. 352–365, 2015.
G. Linden, B. Smith, and J. York, “Amazon.com recommendations: item-to-item collaborative filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76–80, 2003. [Online]. Available: 10.1109/mic.2003. 1167344; https://dx.doi.org/10.1109/mic.2003.1167344.
X. Wu, Y. Huang, and S. Wang, “A New Similarity Computation Method in CF Based RS,” IEEE 86th Vehicular Technology Conference, 2017.
H. Liu, “A new user similarity model to improve the accuracy of CF,” Knowledge-Based Systems, vol. 56, pp. 156–166, 2014.
J. Bobadilla, F. Ortega, and A. Hernando, “A CF similarity measure based on singularities,” Information Processing & Management, vol. 48, p. 204–217, 2012.
G. Vinodhini and R. M. Chandrasekaran, “A comparative performance evaluation of neural network based approach for sentiment classification of online reviews,” Journal of King Saud University - Computer and Information Sciences, vol. 28, no. 1, pp. 2–12, 2016. [Online]. Available: 10.1016/j.jksuci.2014.03.024; https: //dx.doi.org/10.1016/j.jksuci.2014.03.024.
[Y. Wang, “Sentiment and emotion classification over noisy labels,” Knowledge-Based Systems, vol. 111, pp. 207–216, 2016.
W. Zhang, G. Ding, L. Chen, C. Li, and C. Zhang, 2013.
P. Thakor and S. Sasi, “Ontology-based Sentiment Analysis Process for Social Media Content,” Procedia Computer Science, vol. 53, pp. 199–207,2015. [Online]. Available: 10.1016/j.procs.2015.07.295; https: //dx.doi.org/10.1016/j.procs.2015.07.295.
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How to Cite
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Nehete, S.P. and Devane, S.R. 2021. Need of Sentiments Analysis with CF for Quality Recommendations. European Journal of Electrical Engineering and Computer Science. 5, 2 (Mar. 2021), 1-5. DOI:https://doi.org/10.24018/ejece.2021.5.2.284.
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