Over the last several years, sentiment analysis has emerged as one of the most popular applications of machine learning. It enables the identification of a user's attitude from a remark, document, or review. As a result of the development of Big Data, recommender systems (RS) are also finding more use in many aspects of day-to-day living. There are three basic kinds of RS: collaborative filtering, content-based, and hybrid. This article presents a quick description of the recommender systems supplemented with a sentiment analysis module. Sentiment Analysis systems may help recommender systems improve by assessing Web-based reviews.
Kazem, R. Ibrahim & Abdullah, E. Fadhil. (2023). Landscape view of recommender system techniques based on sentiment analysis. International Journal of Nonlinear Analysis and Applications, 14(1), 1539–1546. https://doi.org/10.22075/ijnaa.2022.7138
MLA
Kazem, R. Ibrahim, & Abdullah, E. Fadhil. "Landscape view of recommender system techniques based on sentiment analysis", International Journal of Nonlinear Analysis and Applications, 14, 1, 2023, 1539–1546. doi: 10.22075/ijnaa.2022.7138
HARVARD
Kazem, R. Ibrahim, & Abdullah, E. Fadhil. (2023). 'Landscape view of recommender system techniques based on sentiment analysis', International Journal of Nonlinear Analysis and Applications, 14(1), pp. 1539–1546. doi: 10.22075/ijnaa.2022.7138
CHICAGO
Kazem, R. Ibrahim & Abdullah, E. Fadhil, "Landscape view of recommender system techniques based on sentiment analysis." International Journal of Nonlinear Analysis and Applications, 14 1 (2023): 1539–1546, doi: 10.22075/ijnaa.2022.7138
VANCOUVER
Kazem R. Ibrahim, Abdullah E. Fadhil. Landscape view of recommender system techniques based on sentiment analysis. IJNAA. 2023;14(1):1539–1546. doi: 10.22075/ijnaa.2022.7138
International Journal of Nonlinear Analysis and Applications