Global Journal of Commerce & Management Perspective
Open Access

The Pandemic Effect: Eploring the Financial Crisis through Deep Learning in E-Commerce

Siman Bamforth^{* *}Correspondence: Siman Bamforth, Department of Commerce, University of Lincoln, Manchester, UK, Email:

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Description

The COVID-19 outbreak has had a serious negative impact on a large number of people, leaving them feeling scared, frightened, and other unpleasant emotions. The range and sophistication of people's emotions have increased after the advent of coronavirus vaccinations. In this study, the use of deep learning techniques to perceive and analyses their sentiments has been studied. Social media is currently the most effective means of communicating ideas and emotions, and using social media can provide insight into the thoughts and sentiments of others as well as what is popular. Data science relies heavily on data analysis and visualization. As the number of customers using ecommerce rises, so do the feedback and reviews they share. New customers rely on these reviews when deciding whether or not to purchase a product and since reviews can be displayed falsely and affect supply and demand for certain products, it is crucial to analyze and visualize reviews to understand their true importance in today's e-commerce. The suggested study demonstrated how to analyze and visualize data using techniques that allow for a quicker conceptual understanding of the data, even when the e-commerce data has large dimensions.

The suggested data was assessed using a range of criteria, providing a comprehensive picture of the data and illuminating how the data relate to one another. All correlation and noncorrelation factors were mapped out and examined. In order to make application areas like product quality and customer satisfaction efficient based on the results of modelling, the proposed work provides an idea about observations in sentiments over various arguments and which sentiments are related to each parameter. It also creates the scope for modelling in order to extract some decision-making insights from the data. A digital marketplace or a business strategy that facilitates online business transactions between customers and sellers may be referred to as "e-commerce". E-commerce simultaneously refers to these two ideas. Two e-commerce sites that prioritize giving client’s access to a functional online marketplace for business-tocustomer transactions are mostly online sellers. Southeast Asia serves as the base for both of these platforms. These two distinct platforms are located in the Philippines (B2C). The "business-toconsumer," or "B2C," business model is centered on conducting business and completing transactions with end users, which may involve the selling of goods and services. An electronic marketplace, sometimes referred to as an "e-marketplace," is a virtual gathering place where different buyers and sellers can communicate online and conduct digital transactions. Customers can choose from two main sorts of online markets, generally speaking. Online selling websites are mostly taken as examples of horizontal e-marketplaces; these two platforms are included in this category. This virtual marketplace serves as a one-stop shop for several suppliers and buyers. For individuals who are interested in making purchases from that marketplace, it offers a wide range of products and services from numerous categories. The COVID-19 pandemic in recent years has changed our perception of safety in all facets of our lives and drawn significant attention to the healthcare sector. Removing yourself from your social circle is a good way to stop the coronavirus from spreading.

It is imperative that appropriate safety measures be taken during this time, including as using masks, often washing one's hands, and avoiding needless physical contact with others. They cannot, however, stop the coronavirus outbreak; they can only lower its severity. The only strategy that has been shown to be effective in battling the coronavirus and maybe eliminating it is vaccination. E-commerce platforms have multiplied dramatically in the last several years. As a result, there has been a surge in interest in the technology that evaluates the tone of customer reviews. In this study, a sentiment dictionary, a BERT model, a CNN model, an XLnet model, and context analysis using a machine learning mechanism are used to construct a model for conducting sentiment analysis on product evaluations. The building of the model makes use of these models.