A Clustering Technique for Improving Marketing Strategy in Social Media using Data Mining Approach (International Journal of Engineering and Computer Science, 2017)
On-line Social Networks (SNSs) are today one of the most popular interactive medium to communicate, share and disseminate a considerable amount of human life information. Daily and continuous communications imply the exchange of several types of content, including free text, image, and audio and video data. The huge and dynamic character of these data creates the premise for the employment of web content mining strategies aimed to automatically discover useful information dormant within the data which helps marketing. This paper aims to focus on four important works i.e. identifying the target users, designing of market strategy/plan, Building the marketing network (groups) & Statistical analysis of categories. Influentially of target user has been discussed with real time instances. Categories have been found based on their influence by using clustering technique. Finally, concluded with statistical analysis that includes graphical representation of highly influenced users. Further this paper helps to extract emotional feelings of the user so that any related articles, posts or videos can be posted to that user