Ni, Pin;
Li, Yuming;
Chang, Victor;
(2022)
Recommendation and Sentiment Analysis Based on Consumer Review and Rating.
In:
Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines.
(pp. 1633-1649).
IGI Global: Hershey, PA, USA.
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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.
| Type: | Book chapter |
|---|---|
| Title: | Recommendation and Sentiment Analysis Based on Consumer Review and Rating |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.4018/978-1-6684-6303-1.ch087 |
| Publisher version: | https://doi.org/10.4018/978-1-6684-6303-1.ch087 |
| Language: | English |
| Additional information: | © 2022, IGI Global. This chapter published as an Open Access Chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159904 |
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