Analytics to Preserve the Street Vendor Tradition of India and to Optimize profits post Covid-19

With the advent of the e-commerce markets, the small businesses in India are experiencing a major hit and a loss of customers. Since the medieval times, India is known for its street markets. It is so prominent that it is a cultural representation, and this prompts a considerable number of people to opt for establishment of business on the streets. During the pandemic, the street vendors are experiencing losses to an extent that they are unable to support their families. Our solution to the problem is a web application called 'Street Vendor Mart'. This aims at helping the hard-working street vendors by marketing their business. Say a citizen is walking on the road and finds a street vendor who is toiling under the sun, seeking to earn even the minimum wage. This citizen can help this street vendor through our application, 'Street vendor mart'. The advertisement posted by the citizen will now be recorded on the site and visible to any person who wishes to do some street shopping. If a user wants to shop for some item for cheaper prices, the user can log in to our site and find a list of street vendors around his location to buy the products. The users can visit the vendors near them, shop from them. Thus our 'Street Vendor Mart' is essentially a virtual mall filled with stores by street vendors If two or more vendors are selling the same category of products in the same location, then the gains will not be up to the mark because of the reduction in demands. As a solution to this, our web application runs Data Analytics to find the optimal location for the vendor to sell his/her category of goods which will maximize their profit. © 2022 IEEE.

A Machine Learning Approach for Sentiment Analysis to Nurture Mental Health Amidst COVID-19

During the pandemic, when fresh news content is generated every minute about the widespread of the virus, many conversations revolve around the spread and cure of the contagion. At the hands of a commoner who posts news about COVID-19 on social media, the news may manifest itself to accommodate the said person's fear or negative propaganda which can potentially trigger a mass panic outbreak or can disrupt the mental health of a reader. This research discusses the application of Machine Learning in Sentiment Analysis to classify Tweets about Coronavirus as fear sentiment or panic sentiment. It proposes the idea of a web-based application that caters to filter out the fear-inducing sentiment from a user's daily Twitter feed, thus giving the user accurate and well-spirited information. Textual analysis is performed along with necessary textual data visualization. A substantial accuracy of 91% is achieved in the classification of brief Tweets using the Naïve Bayes method. An accuracy of 74% is achieved using the Logistic Regression classification method for brief tweets. This depicts the advancements in the field of sentimental analysis and sheds light on how it can be employed amidst a challenging situation like the pandemic to preserve mental health. © 2021 ACM.