The week's pick
Reseach Article
Rating Prediction based on Social Sentiment from Textual Reviews using MaxEnt Classifier
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 22 |
| Year of Publication: 2019 |
| Authors: M. Kotinaik, N. Rajeswari |
10.5120/ijca2019919081
|
M. Kotinaik, N. Rajeswari . Rating Prediction based on Social Sentiment from Textual Reviews using MaxEnt Classifier. International Journal of Computer Applications. 178, 22 ( Jun 2019), 34-38. DOI=10.5120/ijca2019919081
Abstract
Lately, we have seen a twist of survey sites. It introduces an incredible chance to share our perspectives for different items we buy. Be that as it may, we face the data over-burdening issue. The most effective method to mine significant data from surveys to comprehend a client's inclinations and make an exact proposal is vital. Customary recommender systems (RS) think about certain variables, for example, client's buy records, item classification, and geographic area. In this work, we propose an algorithm called MaxEnt classifier to improve prediction precision in recommender systems. Right off the bat, we propose a social client wistful estimation approach and ascertain every client's conclusion on things/items. Furthermore, we consider a client's own wistful qualities as well as mull over relational nostalgic impact. At that point, we think about item notoriety, which can be induced by the wistful disseminations of a client set that mirror clients' exhaustive assessment. Finally, we combine three components client assumption comparability, relational nostalgic impact, and thing's notoriety closeness into our recommender framework to make a precise rating prediction. We direct a presentation assessment of the three nostalgic factors on a genuine data gathered from IMDB.
References
- R. Salakhutdinov, and A. Mnih, “Probabilistic matrix factorization,” in NIPS, 2008.
- X. Yang, H. Steck, and Y. Liu, “Circle-based recommendation in online social networks, ” in Proc. 18th ACM SIGKDD Int. Conf. KDD, New York, NY, USA, Aug. 2012, pp. 1267–1275.
- M. Jiang, P. Cui, R. Liu, Q. Yang, F. Wang, W. Zhu, and S. Yang, “Social contextual recommendation,” in proc. 21st ACM Int. CIKM, 2012, pp. 45-54.
- M. Jamali and M. Ester, “A matrix factorization technique with trust propagation for recommendation in social networks,” in Proc. ACM conf. RecSys, Barcelona, Spain. 2010, pp. 135-142.
- Z. Fu, X. Sun, Q. Liu, et al., “Achieving Efficient Cloud Search Services: Multi-Keyword Ranked Search over Encrypted Cloud Data Supporting Parallel Computing,” IEICE Transactions on Communications, 2015, 98(1):190-200.
- G. Ganu, N. Elhadad, A Marian, “Beyond the stars: Improving rating predictions using Review text content,” in 12th International Workshop on the Web and Databases (WebDB 2009). pp. 1-6.
- J. Xu, X. Zheng, W. Ding, “Personalized recommendation based on reviews and ratings alleviating the sparsity problem of collaborative filtering,” IEEE International Conference on e-business Engineering. 2012, pp. 9-16.
- X. Qian, H. Feng, G. Zhao, and T. Mei, “Personalized recommendation combining user interest and social circle,” IEEE Trans. Knowledge and data engineering. 2014, pp. 1763-1777.
- H. Feng, and X. Qian, “Recommendation via user’s personality and social contextual,” in Proc. 22nd ACM international conference on information & knowledge management. 2013, pp. 1521-1524.
- Z. Fu, K. Ren, J. Shu, et al., “Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement,” IEEE Transactions on Parallel & Distributed Systems, 2015:1-1.
- D.M. Blei, A.Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of machine learning research 3. 2003, pp. 993-1022.
- W. Zhang, G. Ding, L. Chen, C. Li , and C. Zhang, “ Generating virtual ratings from Chinese reviews to augment online recommendations,” ACM TIST, vol.4, no.1. 2013, pp. 1-17.
- Z. Xia, X. Wang, X. Sun, and Q. Wang, “A Secure and Dynamic Multi-keyword Ranked Search Scheme over Encrypted Cloud Data,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2, 2015, pp. 340-352.
- J. Weston, R. J. Weiss, H. Yee, “Nonlinear latent factorization by embedding multiple user interests,” 7th ACM, RecSys, 2013, pp. 65-68.
- J. Huang, X. Cheng, J. Guo, H. Shen, and K. Yang, “ Social recommendation with interpersonal influence,” in Proc. ECAI, 2010, pp. 601-606.
- Y. Lu, M. Castellanos, U. Dayal, C. Zhai, “Automatic construction of a context-aware sentiment lexicon: an optimization approach,” World Wide Web Conference Series. 2011, pp. 347-356.
- T. Kawashima, T. Ogawa, M. Haseyama, “A rating prediction method for e-commerce application using ordinal regression based on LDA with multi-modal features,” IEEE 2nd Global Conference on Consumer Electronics (GCCE). 2013, pp. 260-261.
- K.H. L. Tso-Sutter, L. B. Marinho, L. Schmidt-Thieme, “Tag-aware recommender systems by fusion of collaborative filtering algorithms,” in Proceedings of the 2008 ACM symposium on Applied computing, 2008, pp. 1995-1999.
- B. Wang, Y. Min, Y. Huang, X. Li, F. Wu, “ Review rating prediction based on the content and weighting strong social relation of reviewers,” in Proceedings of the 2013 international workshop of Mining unstructured big data using natural language processing, ACM. 2013, pp. 23-30
- F. Li, N. Liu, H. Jin, K. Zhao, Q. Yang, X. Zhu, “Incorporating reviewer and product information for review rating prediction,” in Proceedings of the Twenty-Second international joint conference on Artificial Intelligence, 2011, pp. 1820-1825..
Index Terms
Keywords