An Analytical Based Model For Remarking Online Conversations | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Volume 24, Issue 1, March 2024, Pages 105-115 PDF (569.03 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2024.261424.1314 | ||
| Author | ||
| Eman Elsayed Mahmoud* | ||
| computer science department, CIC | ||
| Abstract | ||
| The massive amount of social media data is considered as an effective resource to extract valuable knowledge. Nowadays, the social media analytical became an advanced informatics tool for collecting, monitoring, and analyzing data. So, it supports the business needs for improving the product and service in order to increase their profit. This paper proposes an analytical model for remarking online conversation and estimating the customer’s perception based on the machine learning techniques. In addition, it investigates the impact of brand community features on the customer’s perception. Based on that, the online conversation is automatically remarked by the degree of conversation polarity as well as the impact of brand community. Finally, The findings emphasized that the brand community features have an impact on the customer’s perception in percentage up to 45.6%. Also, it realized that in many statuses the remarks provide a significant feedback that forces the business for making decision and enhancing capabilities. | ||
| Keywords | ||
| social media analytical; Conversation Polarity; automatic remark | ||
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