Abstract
In this paper, buyers are looking for reviews of the products before purchasing them. In view of this, online shopping platforms are encouraging their customers to provide reviews on products that would help future customers and the service provider to enhance their services. These reviews are normally in natural language mostly in the English language. These are used to analyze and provide data that is used for repairing and building new products because most services are unable to review consumers' reviews at the same time regularly, so they need mining tools to learn about those reviewers, which is what consumers need for their goods. Users review assessments for upgrading their products. Reviews are analyzed by customers to decide whether to purchase or not to purchase. The main objectives of this study are to develop a recommendation system based on customer reviews, develop a dataset of customer reviews from Amazon and eBay, analyze the reviews to create a database of products, develop an algorithm for generating positive/negative scores for a product, develop a method for gathering user requirements in natural language and to identify the main product, and also develop an algorithm to match the user request with the product and generate recommendations. The study concluded that the selected program was suitable for analyzing and managing the review data.
Highlights
Natural Language Processing (NLP) is commonly used for analyzing customers’ feedback
We propose a QoS conscious resource allocation approach based on user ranking implicit input in Mobile Edge Computing (MEC) (Puja Das, and Asik Rahaman Jamader, 2019)
The aim of this paper is to provide an overview of mobile context-aware recommender systems
Summary
Natural Language Processing (NLP) is commonly used for analyzing customers’ feedback. In natural language processing (NLP), the goal is to make computers understand the unstructured text and retrieve meaningful pieces of information from it. Social networking is defined as the use of Internet-based social media platforms to keep in touch with friends, family, coworkers, customers, or clients. To access social media platforms, the majority of users use web-based software on their (Desktops) and (Laptops). They use their smartphones and tablets to download social networking applications. The recommendations were based on the community's collective experience over the last decade It went into the core algorithms used in classification and recommendation systems. The negative affect is that if the customer can’t get the products very well, it means there is something wrong with their products. (Kamaran Faraj, 2019) E-recommendation by (semi restricted search engine and restricted search engine) solve the inconvenience of traditional recommendation because of E-recommendation is paperless, less time consume, accurate, reduce health and safety hazards and etc. regarding the TELOS categories E-recommender output outcome is much higher and it is feasible than the traditional recommender
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