Ehsan Kashi; Mehri Shahriari
Abstract
News and rumors about the prevalence of corona virus on social media have a significant impact on people. The aim of this study is to examine the topics discussed by people about corona disease in social media from the beginning of corona prevalence to the present day. The research data were collected ...
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News and rumors about the prevalence of corona virus on social media have a significant impact on people. The aim of this study is to examine the topics discussed by people about corona disease in social media from the beginning of corona prevalence to the present day. The research data were collected from people’s comments in posts related to Corona News on Instagram and analyzed using the method of text mining and clustering. Based on the results of the research, the topics of discussion of the citizens were divided into 10 clusters, which are: Lack of sanitary equipment, lack of attention to quarantine, news and rumors, mental condition, information about symptoms, prevention, control and treatment, government and public actions, lack of personal hygiene, death rate in patients and burial, closure of educational activities And economic problems. Then they were compared with the issues in December and January, when some issues such as access to vaccines, hourly traffic restrictions and the mutated virus were added to the concerns of the people, and some of them were addressed by government measures.
Azim Zarei; Mehri Shahriari
Abstract
Customer satisfaction requires the customer to be happy both in daily and long-term and global interactions. People's opinions about the products of a company on websites and social media can provide useful information for companies to evaluate customer satisfaction. In this research, using the methodology ...
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Customer satisfaction requires the customer to be happy both in daily and long-term and global interactions. People's opinions about the products of a company on websites and social media can provide useful information for companies to evaluate customer satisfaction. In this research, using the methodology text mining and k- means clustering, customers' opinions about the three brands of Snowa, Pakshoma and Parskhazar from domestic appliances and comments on the three brands of Samsung, LG, and Tefal from external home appliances in the website of Digikala.com were analyzed. The results of this study show that dissatisfaction factors were clustered in six attributes, product failure, and price proportions with performance, efficiency, design, manufacturing quality and after-sales services. In domestic appliances, the most dissatisfaction factors were the product failure, price proportions with performance, manufacturing quality, after-sales service, efficiency, and design. And the factors causing dissatisfaction in external home appliances were manufacturing quality, product failure, design, after-sales service, price proportions with performance, and efficiency.
