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WeatherPon: A Weather and Machine Learning-Based Coupon Recommendation Mechanism in Digital Marketing2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI)10.1109/SEAI59139.2023.10217467
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Eigenvector-based Graph Neural Network Embeddings and Trust Rating Prediction in Bitcoin NetworksProceedings of the Third ACM International Conference on AI in Finance10.1145/3533271.3561793

Recommendation and Sentiment Analysis Based on Consumer Review and Rating

Pin Ni, Yuming Li, Victor Chang

Source Title: Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines

ISBN13: 9781668463031|ISBN10: 1668463032|EISBN13: 9781668463048

DOI: 10.4018/978-1-6684-6303-1.ch087

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MLA

Ni, Pin, et al. "Recommendation and Sentiment Analysis Based on Consumer Review and Rating." Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, IGI Global, 2022, pp. 1633-1649. https://doi.org/10.4018/978-1-6684-6303-1.ch087

APA

Ni, P., Li, Y., & Chang, V. (2022). Recommendation and Sentiment Analysis Based on Consumer Review and Rating. In I. Management Association (Ed.), Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines (pp. 1633-1649). IGI Global. https://doi.org/10.4018/978-1-6684-6303-1.ch087

Chicago

Ni, Pin, Yuming Li, and Victor Chang. "Recommendation and Sentiment Analysis Based on Consumer Review and Rating." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, 1633-1649. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6303-1.ch087

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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.