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.
Highlights
Online review and rating, as the two most important customer reference factors in online shopping platforms, have a greater influence on consumers’ willingness to buy
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
This paper will compare the conventional machine learning classification model and the neural network classification model through experiments to obtain a better solution in the sentiment analysis of text reviews
Summary
As the two most important customer reference factors in online shopping platforms, have a greater influence on consumers’ willingness to buy. The study is based on beer rating data and recommends beer products through the Spark-ALS collaborative filtering algorithm and compared 10 classification models including conventional machine learning and deep learning for consumer review analysis. We combine customers’ review text data and rating data to support the construction of recommendation model and improve its effectiveness. Experiments have shown that our method achieves effective performance on beer product recommendation task; 2. We conducted a relatively comprehensive literature review of previous research in customer review mining, product rating analysis, and provided some technical and application analysis and suggestions for related business intelligence fields. 5. Section 6 illustrates the limitations of the study, discusses, and analyzes the value of related tasks from both technical and application perspectives.
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