  Recommender systems nowadays are playing an important role in the delivery of
services and information to users. Sentiment analysis (also known as opinion
mining) is the process of determining the attitude of textual opinions, whether
they are positive, negative or neutral. Data sparsity is representing a big
issue for recommender systems because of the insufficiency of user rating or
absence of data about users or items. This research proposed a hybrid approach
combining sentiment analysis and recommender systems to tackle the problem of
data sparsity problems by predicting the rating of products from users reviews
using text mining and NLP techniques. This research focuses especially on
Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic
(OCA) dataset. Our system was efficient, and it showed a good accuracy of
nearly 85 percent in predicting rating from reviews
