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Impact of Review Rank on overall Product Rank for given Product Rating
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Gelişmiş ve gelişmekte olan ülkeler için ithalat ve ihracat faaliyetlerinin dengeli ve verimli bir şekilde gerçekleştirilmesi ülke ekonomisine önemli katkılar sağlamaktadır. Bu nedenle ithalat ve ihracat faaliyetlerinin dengeli ve verimli bir şekilde gerçekleştirilmesinde uluslararası taşımacılık faaliyetleri oldukça önemli bir rol oynamaktadır. Ar...
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... To enhance Amazon Searchʹs relevance ranking, the study employed a diverse set of relevance algorithms, emphasizing the significant impact on customer satisfaction and financial outcomes [123]. Various methods for analyzing consumer opinions on platforms like Amazon.com were explored, introducing a hybrid approach that effectively ranked products based on text reviews, Question Answer (QA) data, and star ratings, enhancing sales predictions [124]. The study addressed the financial and reputational impact of product issues in over-the-counter (OTC) pain relief products, utilizing Amazonʹs product reviews to identify safety and efficacy concerns through ʺsmoke wordʺ dictionaries and sentiment analysis [125]. ...
In recent years, e-commerce platforms have replaced conventional marketplaces. People are fast adopting online shopping since they can purchase from home. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer evaluations, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges. This study covers 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the bulk of existing research relies on Amazon user reviews. Additionally, recent research has stressed the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates researchers' expanding interest and the field's continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.
E-commerce websites, besides selling products and services, pay ample emphasis on providing a platform for consumers to share their opinions about past and potential purchases. They share such opinions as product reviews (star ratings, plain text, etc) and answering product related questions (Q&A data). There are several machine learning and classification approaches available to scrutinize this review data, e.g., algorithms based on Entropy measures, Bilinear Similarity, stochastic methods, etc. In this paper, we review some of the prevalent review classification techniques and present a hybrid approach, involving Singular Value Decomposition (SVD), Entropy and Bilinear Similarity measures, that uses heterogeneous product data and simultaneously analyze and rank products for customers. With experimental results, we show that our approach effectively ranks products using (1) text reviews (2) Q&A data (3) five-star rating of products and has 10% improved prediction accuracy as compared to the individual approaches. Also, using SVD, we achieve a 35% runtime efficiency for our algorithm while only sacrificing 1% of the prediction accuracy.
