Monireh Hosseini; Mahjoob Sadat Navabi
Volume 10, Issue 1 , July 2023, , Pages 1-15
Abstract
With the development and widespread use of social networks among people, high-volume data is produced and the analysis of this data can be useful in many areas, including people's daily lives. Classification of this volume of data using traditional methods is a very difficult, time-consuming, and low-accuracy ...
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With the development and widespread use of social networks among people, high-volume data is produced and the analysis of this data can be useful in many areas, including people's daily lives. Classification of this volume of data using traditional methods is a very difficult, time-consuming, and low-accuracy task, therefore, using sentiment analysis techniques, people's opinions can be effectively summarized and categorized. To this end, we propose an algorithm that combines Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The reason for combining the two algorithms is that the GSA has a good ability to search overall, but in the last iterations, it has a low speed in exploiting the search space. Since the PSO algorithm has a special ability to exploit the search space, this algorithm is used in the exploitation phase to solve the problem. The accuracy obtained from our proposed algorithm (PSO-GSA) shows an improvement in the accuracy of the GSA algorithm.
Monireh Hosseini; Zohreh Tammimy; Elnaz Galavi
Abstract
Social networks provide marketing managers and businesses with opportunity to target their customers. By understanding the demographics of users, marketing managers can offer suitable products and services. Although direct questioning can be drawn upon to solicit users’ demographics such as age, ...
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Social networks provide marketing managers and businesses with opportunity to target their customers. By understanding the demographics of users, marketing managers can offer suitable products and services. Although direct questioning can be drawn upon to solicit users’ demographics such as age, some customers due to privacy concerns do not like to reveal their personal information and, it cannot come in handy for potential customer identification. The huge amount of data social networks generate can solve this problem. Previous studies in the prediction of demographic characteristics suffer some limitations because they were mainly text based and hence, language-bound. This study investigates how some interactive data can predict users’ age. Further, it examines if classification methods can be used for age prediction. The results revealed that the number of friends, number of opposite sex friends, number of comments received, and number of photos which users share can predict users’ age. Also, a linear relationship between interactive data and users’ age was found.