Arabic Opinion Mining Using a Hybrid Recommender System Approach
This addresses data sparsity for Arabic-language recommender systems, but it is incremental as it applies a hybrid approach to a specific domain.
The research tackled data sparsity in recommender systems by predicting product ratings from Arabic user reviews using sentiment analysis and NLP, achieving about 85% accuracy on the OCA dataset.
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