ABSTRACT
With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.
ABSTRACT
Depression is a medical illness that affects the way you think and how you react. It is a serious medical issue that impacts the stability of the mind. Depression occurs at many stages and situations. With the help of classification, the stage of depression the person is in can be tried to categorize. Nowadays, many users are sharing their views on social media, and it became a platform for knowing people around us. From the data that is shared on social media, the depressing posts are being classified using machine learning techniques. With these reports collected, the depressed person might be helped from making any sudden decisions. So, in our research study, the large datasets of the people in depression during the COVID-19 pandemic situations is analyzed and not in pandemic situations. Here to analyze the data, the neural networks have been trained with the current pandemic analysis report, and it has given a prediction that the people are less likely to get depressed when they are not in a pandemic situation like COVID-19. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.