ABSTRACT
The rise of technological adoption of video-based information distribution accelerated by the COVID-19 pandemic leads to a substantial increase in video streaming on-demand service users. Video streaming services such as Twitch, and YouTube have enormous amount of active users so the system developers need to develop a system capable of handling numerous requests. The increase of video streaming on-demand service users poses a challenge for system developers to guarantee the Quality of Experience (QoE) for all system users. Said surge of users also resulted in high resource usage to serve each user while securing QoE. In order to ensure the QoE on video streaming on-demand service, this work develops the system using the waterfall model that implements a distributed approach for the video streaming service system. The system has reverse proxy and load balancing capabilities to forward and equally distribute HTTP requests and uses DASH to segment the video file to stream the video without sending the whole file. The developed system for this work subjectively ensures the QoE with a mean opinion score (MOS) of 4.434 from 53 respondents. © 2022 IEEE.
ABSTRACT
In the recent years, especially after covid-19 became a thing, people started finding information about Retail On- Demand Services online rather than relying on local contacts. This makes it a lot easier for people to find and book services online, rather than going out and booking them in person. So, a Django-based Web application with a real time database deployed on cloud that provides details of some service providers (Carpenters, Electricians etc.) in region of Kanuru (Vijayawada, India) has been designed. Data are collected from multiple sources using a scraping code that Beautiful Soup framework is used and stored in PostgreSQL database that is later deployed on Cloud platforms. This helps people from this region to access information quickly and help them book these On-Demand Services Faster. © 2022 IEEE.
ABSTRACT
Electronic word of mouth (e-WOM) is a vital channel for the exchange of customer-generated content. As the e-WOM messages created by consumers pile all around the Web, they generate an unbiased voice about products and services. With their high-level production in online environments, e-WOM message contributions go far beyond consumer decision-making. They become a vital source for gaining insights on designing and improving marketing offerings. The purpose of the study is to analyze service improvement opportunities for subscription-based video-on-demand (sVoD) services by exploring customer-generated eWOM messages. In addition to this, the study aims to comprehend the effects of the COVID-19 pandemic on expectations, real feelings, and attitudes of customers towards its subscription-based video-on-demand services and to compare these emotions with those in the pre-pandemic period. Acting on customer-generated e-WOM messages for sVoD services, the paper provides a real-time analysis of monitoring customer needs and wants in a fast-moving service environment with topic-based sentiment analysis. The main procedures include data extraction and pre-processing, topic modeling, sentiment analysis, and opportunity analysis. When pre-covid and post-covid sentiments are compared, it is found that all sentiment scores have decreased, except for content diversity. The rich content offered by Amazon has led subscribers to take step to post positive comments about the platform. Addtionally, the results show possible service improvement opportunities in streaming quality, TV series content selection, use of commercials, and customer value generation in sVoD service encounters. The study identifies service improvement opportunities using data mining technology, which can provide a more in-depth understanding of consumer perceptions of marketing offerings and service quality. In addition, it analyzes the perceptions of consumers toward sVoD services in times of the Covid-19 pandemic.