Topic and Sentiment Analysis Matrix Factorization on Rating Prediction for Recommendation

Wang, Ben; Fang, Guan-Shen; Kamei, Sayaka

doi:10.1109/candarw51189.2020.00037

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Wang et al [10] proposed sentiment matrix factors, sentiment scores and the reviews-based recommendation model to predict suitable content. Initially, they analyzed various topics and reviewed comments by applying the lexicon construction and Latent Dirichlet Allocation (LDA) methods.…”

Section: Related Workmentioning

confidence: 99%

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A New Fuzzy Lexicon Expansion and Sentiment Aware Recommendation System in e-Commerce

Manikandan¹,

P²,

S³

2023

IJACSA

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Customers' feedbacks are necessary for an online business to enrich themselves. The customers' feedback reflects the quality of the products and the e-commerce services. The companies are in a position to concentrate more and analyze the customers' feedback or reviews carefully by applying new techniques for predicting the current trends, customers' expectations, and the quality of their services. The e-business will succeed when one accurately predicts customer purchase patterns and expectations. For this purpose, we propose a new fuzzy logic incorporated sentiment analysis-based product recommendation system to predict the customers' needs and recommend suitable products successfully. The proposed system incorporates a newly developed sentiment analysis model which incorporates the classification through fuzzy temporal rules. Moreover, the basic level data preprocessing activities such as stemming, stop word removal, syntax analysis and tokenization are performed to enhance the sentiment classification accuracy. Finally, this product recommendation system recommends suitable products to the customers by predicting the customers' needs and expectations. The proposed system is evaluated using the Amazon dataset and proved better than the existing recommendation systems regarding precision, recall, serendipity and nDCG.

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Section: Related Workmentioning

confidence: 99%

“…b) nDCG: This metric is useful for identifying the user's liked products and recommending them for purchase. The ncDCG value helps check the correctness of the recommended product described in (10).…”

Section: B Performance Metricsmentioning

confidence: 99%