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recommender system nowadays is used to deliver services and information to users. A recommender system is suffering from problems of data sparsity and cold start because of insufficient user rating or absence of data about users or items. This research proposed a sentiment analysis system work on user reviews as an additional source of information to tackle data sparsity problems. Sentiment analysis system implemented using NLP techniques with machine learning to predict user rating form his review; this model is evaluated using Yelp restaurant data set, IMDB reviews data set, and Arabic qaym.com restaurant reviews data set under various classification model, the system was efficient in predicting rating from reviews.
Keywordsrecommender systems, sentiment analysis, opinion mining, natural language processing, text classification.
 Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B.Kantor, Recommender systems handbook. Springer, 2011.
 R. Martinas, Garcia," A collaborative filtering approach based on user's reviews," in the proceeding of Brazilian conference on intelligent systems, pp: 204-209, 2014.
 F.O Isinkaye, Y.O. Folajimi, B.A. Ojokoh, 2015, “Review: Recommendation systems: principles, methods, and evaluation,” Egyptian Informatics Journal, Issue 16, pp: 261-273.
 L. Lu, M. Medo, C. Ho, Yeung, Y. Zhang, Z. Zhang, Tao, Zhou,”recommender systems," Physics Reports, Vol 519, pp 1-49,2012.
 C.weng, S.chi-Fai, F.Chung, "Integrating collaborative filtering and sentiment analysis: A rating inference approach," in the proceeding of ECAI workshop on recommender systems, pp: 62-66, 2006.
 W.Medhat, A. Hassan, H. Korashy, " sentiment analysis algorithms and applications a survey," Ain- shams engineering journal, vol5, pp: 1003-1113, 2014.
 M.Issa, V. Piek "A lexicon model for deep sentiment analysis and opinion mining applications," decision support system journal, vol 2, pp: 680-688, 2012.
 P.Turneg, "Thumbs up or thumbs down? Sentiment orientation applied to unsupervised classification of reviews", in the proceeding of the 40th annual meeting of the association for computational linguistics, pp: 417-424, 2002.
 R. Qumsiyeh, Y.Ng. ,"predicting the rating of multimedia items for making personalized recommendations," in the proceeding of the 35th international ACU SIGIR conference on research and development information retrieval, pp" 475-484, 2012.
 H. Kim, K. Han, J. Cho, J.Hong, "movie mine: personalized movie content search by utilizing user comments," IEEE transactions and consumer electronics, volume 58, pp: 1416-1426, 2012.
 G. Ganu, Y.Kakadkar, A. Marian, "Improving the quality of prediction using textual information in online user reviews," information system journal, vol 38, issue 1, pp: 1-15, 2013.
 D.Alahmadi, X.Zeng, " ISITS: implicit social trust and sentiment based approach to recommender systems," expert systems with applications journal, vol. 42, pp: 8840-8849, 2015.
 A. Bhardwaj, Y. Narayan, V. Anarj, Pawnan, M. Dutta, " sentiment analysis for Indian stock market predictor using Sensex and nifty," in proceeding 4th international conference on eco-friendly computing and communication systems, pp: 85-91, 2015.
 M. Giatsoglou, M.Vozalis, K.Dimantras, A. Vakali, G.Sarigiannidis, K. Chatzisavvas, " sentiment analysis leveraging emotions and word embedding's," expert systems and applications journal, volume 69, pp: 214-224, 2017.