IJMTES – Paper Presented in: ‘2 day State Level workshop on Cyber Fest 17’, conducted by: ‘Department of Computer Engineering, Marathwada Mitra Mandal College of Engineering, Pune’ on 22-23 Feb 2017

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : RATING PREDICTION FROM TEXTUAL REVIEWS BASED ON SENTIMENTAL ANALYSIS

Author’s Name : Chaitrali G Kulkarni | Sanyogita S Kumthekar | Piyusha D Pardeshi | Yashomati G Panchade | Swati Shekapureunnamed

Paper Presented in : ‘2 day State Level workshop on Cyber Fest 17’, conducted by: ‘Department of Computer Engineering, Marathwada Mitra Mandal College of Engineering, Pune’ on 22-23 Feb 2017

Volume 04 Issue 05 2017

ISSN no:  2348-3121

Page no: 43-48

Abstract – In recent years, we have witnessed enumerous reviews from different review websites. These review websites gives a great opportunity to share our viewpoints for various products we purchase. Due to loads of information, we face problem in mining valuable information from reviews to understand a user’s preferences and make an accurate recommendation. Traditional recommender systems (RS) consider some factors, such as product category, user’s purchase records and geographic location. In this work, we propose a sentiment- based rating prediction method (RPS) to improve prediction accuracy in recommender systems along with Dynamic question list for review generation. We use a social user sentimental measurement approach to calculate user’s sentiment on items/products along with that we also consider user’s interpersonal sentimental influence and product reputation, which can be inferred from user’s reviews. At last, we combine three factors-user sentiment similarity, item reputation similarity and interpersonal sentimental influence into our recommender system to make an accurate rating prediction. We also generate Dynamic question list for easy review generation.

Keywords— Item reputation, Reviews, Rating prediction, Recommender system, Sentiment influence, User sentiment, HDFS (Hadoop Distributed File System), HIVE,RPC(Remote Procedure Calls),ACLs(Access Control Lists)

References

  1. R. Salakhutdinov, and A. Mnih, “Probabilistic matrix factorization,” in NIPS, 2008.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. D.M. Blei, A.Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of machine learning research 3. 2003, pp. 993-1022.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item- based collaborative filtering recommendation algorithms,” in Proc.10th International Conference on World Wide Web, 2001, pp. 285-295.
  13. K. K. Fletcher, X.F Liu, “A collaborative filtering method for personalized preference-based service recommendation,” 2015 IEEE International Conference on Web Services (ICWS), 2015, pp. 400-407.
  14. L. Qu, G. Ifrim, G. Weikum, “The bag-of-opinions method for review rating prediction from sparse text patterns,” in Proc. 23rd International Conference on Computational Linguistics, 2010, pp. 913–921.
  15. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl, “Group-Lens: an open architecture for collaborative filtering of net news,” in Proc. CSCW 1994. pp. 175–186.
  16. Y. Ren, J. Shen, J. Wang, J. Han, and S. Lee, “Mutual Verifiable Provable Data Auditing in Public Cloud Storage,” Journal of Internet Technology, vol. 16, no. 2, 2015, pp. 317-323.
  17. K. Zhang, Y. Cheng, W. Liao, A. Choudhary, “Mining millions of reviews: a technique to rank products based on importance of reviews,” in Proceedings of the 13th International Conference on Electronic Commerce, Aug. 2011, pp. 1-8.
  18. W. Luo, F. Zhuang, X. Cheng, Q. H, Z. Shi, “Rateable aspects over sentiments: predicting ratings for unrated reviews,” IEEE International Conference on Data Mining (ICDM), 2014, pp. 380-389.
  19. B. Pang, Bo, L. Lee, and S. Vaithyanathan, “Thumbs up? sentiment classification using machine learning techniques,” in Proc. EMNLP, 2002, pp. 79-86.
  20. D. Tang, Q. Bing, T. Liu, “Learning semantic representations of users and products for document level sentiment classification,” in Proc. 53th Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, July 26-31, 2015, pp. 1014–1023.
  21. T. Nakagawa, K. Inui, and S. Kurohashi, “Dependency tree-based sentiment classification using CRFs with Hidden Variables,” NAACL, 2010, pp.786-794.
  22. H. Ma, H. Yang, M. R. Lyu, and I. King, “SoRec: Social recommendation using probabilistic matrix factorization,” in Proc.17th ACM CIKM, Napa Vally, CA, USA, 2008, pp.931- 940.
  23. H. Kanayama and T. Nasukawa, “Fully automatic lexicon expansion for domain-oriented sentiment analysis,” in EMNLP’06, pp. 355-363.
  24. K. Lee, and K. Lee, “Using dynamically promoted experts for music recommendation,” IEEE Trans. Multimedia, vol. 16, no. 5, 2014.
  25. X. Ding, B. Liu, and P. S. Yu, “A holistic lexicon-based approach to opinion mining,” in WSDM ’08, pp. 231-240.
  26. Y. Zhang, G. Lai, M. Zhang, Y. Zhang, Y. Liu, S. Ma, “Explicit factor models for explainable recommendation based on phrase-level sentiment analysis,” in proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, 2014.
  27. X. Lei, and X. Qian, “Rating prediction via exploring service reputation,” 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP), Oct 19-21, 2015, Xiamen,China. Pp1-6