Framework for Social Media Analytics based on Multi-Criteria Decision Making (MCDM) model

2019 ◽  
Vol 79 (5-6) ◽  
pp. 3913-3927
Author(s):  
Muruganantham A. ◽  
G. Meera Gandhi
Author(s):  
Muhammad Sabir Ramadhan Et al.

In the era of the Industrial Revolution 4.0, social media is very influential in marketing company products, therefore companies interested in using social media need the role of content creators and employees. This is because the selection of prospective employees for content creators is still manual, and the selection of employees who are still related to family leads to an objective selection. Information technology and decision support systems need to be used as a tool to determine the selection of quality content creation staff using the WASPAS method, a combination of the WSM and WPM methods. By assigning weights to each criterion and then conducting a ranking evaluation process, this method can be used to solve the "multi-criteria decision making" problem. The result of this research is to find content creators who meet the standards of the WASPAS analysis method.


Author(s):  
Ravindra Kumar Singh ◽  
Harsh Kumar Verma

Online food delivery applications have gained significant attention in the metropolitan cities by diminishing the burden of traveling and waiting time by offering online food delivery options for various dishes from many such restaurants. Users enjoy these services and share their experiences and opinions on social media platforms that impact the trust of customers and change their purchasing habits. This drastic revolution of user activities is an opportunity for targeted social marketing. This research is based on Twitter's data and aimed to identify the influence of social media in food delivery e-commerce businesses including decision making, marketing strategy, consumer behavior analysis, and improving brand reputation. In this article, the authors proposed an Apache Spark-based social media analytics framework to process the tweets in real time to identify the influences of generated insights on e-commerce decision making. The experimental analysis highlighted the exponentially grown influence of social media in food delivery e-commerce portals in past years.


Author(s):  
Ravindra Kumar Singh ◽  
Harsh Kumar Verma

Online food delivery applications have gained significant attention in the metropolitan cities by diminishing the burden of traveling and waiting time by offering online food delivery options for various dishes from many such restaurants. Users enjoy these services and share their experiences and opinions on social media platforms that impact the trust of customers and change their purchasing habits. This drastic revolution of user activities is an opportunity for targeted social marketing. This research is based on Twitter's data and aimed to identify the influence of social media in food delivery e-commerce businesses including decision making, marketing strategy, consumer behavior analysis, and improving brand reputation. In this article, the authors proposed an Apache Spark-based social media analytics framework to process the tweets in real time to identify the influences of generated insights on e-commerce decision making. The experimental analysis highlighted the exponentially grown influence of social media in food delivery e-commerce portals in past years.


Author(s):  
Supun Abeysinghe ◽  
Isura Manchanayake ◽  
Chamod Samarajeewa ◽  
Prabod Rathnayaka ◽  
Malaka J. Walpola ◽  
...  

2020 ◽  
Vol 20 (4) ◽  
pp. 552-586
Author(s):  
MICHAEL J. MAHER ◽  
ILIAS TACHMAZIDIS ◽  
GRIGORIS ANTONIOU ◽  
STEPHEN WADE ◽  
LONG CHENG

AbstractRecent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning – for example, for decision making – could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.


Author(s):  
Riya M. Hate ◽  
Vaishnavi T. Naik

Social media is a broad spectrum of process like data collection, data storage into databases and preparation of data which is utilized for research, decision making, marketing campaign with the aid of various tools and algorithms to measure the performance of a product. Analyzing social media to collect data and planning strategy around them has become one of the major business activity. Topics covered in this papers would be applications and impact of social media, challenges faced while retrieving data and preparing data for business intelligence, it also mentions some tools and algorithm.


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