Recommendation and Sentiment Analysis Based on Consumer Review and Rating

Pin Ni, Yuming Li, Victor Chang

Source Title: International Journal of Business Intelligence Research (IJBIR)11(2)

ISSN: 1947-3591|EISSN: 1947-3605|EISBN13: 9781799807162|DOI: 10.4018/IJBIR.2020070102

MLA

Ni, Pin, et al. "Recommendation and Sentiment Analysis Based on Consumer Review and Rating." IJBIR vol.11, no.2 2020: pp.11-27. http://doi.org/10.4018/IJBIR.2020070102

APA

Ni, P., Li, Y., & Chang, V. (2020). Recommendation and Sentiment Analysis Based on Consumer Review and Rating. International Journal of Business Intelligence Research (IJBIR), 11(2), 11-27. http://doi.org/10.4018/IJBIR.2020070102

Chicago

Ni, Pin, Yuming Li, and Victor Chang. "Recommendation and Sentiment Analysis Based on Consumer Review and Rating," International Journal of Business Intelligence Research (IJBIR) 11, no.2: 11-27. http://doi.org/10.4018/IJBIR.2020070102

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Abstract

Accurate analysis and recommendation on products based on online reviews and rating data play an important role in precisely targeting suitable consumer segmentations and therefore can promote merchandise sales. This study uses a recommendation and sentiment classification model for analyzing the data of beer product based on online beer reviews and rating dataset of beer products and uses them to improve the recommendation performance of the recommendation model for different customer needs. Among them, the beer recommendation is based on rating data; 10 classification models are compared in text sentiment analysis, including the conventional machine learning models and deep learning models. Combining the two analyses can increase the credibility of the recommended beer and help increase beer sales. The experiment proves that this method can filter the products with more negative reviews in the recommendation algorithm and improve user acceptance.