Indian Journal of Science and Technology

Abstract

Background/Objectives: The Traditional Medias such as television, newspapers and radios become less qualified because they are not much preferred. The traditional techniques become inexperienced as because they do not consider social relation data. While, the existing social recommendation approaches has not been fully considered to provide any useful information as they only classify the tweets and there is no proper alert to rescue people. Methods/Statistical analysis: This paper is to investigate the social problems on the basis of both economically and emotionally using twitter; summarizing the classified tweets into useful information used for both increasing revenue and cutting costs. Then in the proposed approach the particle filter mechanism is used to extract keywords from tweets. Further it uses stemming algorithm which is used for reducing variant forms of a word to a common form. Data are split and stored in the Data node and the index is maintained by the Name node. Findings: The factual results and analysis manifest that the proposed method significantly outperforms the existing approaches. Applications/Improvements: The importance of the social networks with reference to their effective usage especially during natural calamity has been highlighted and also its usefulness for people to get adapted to the society has been emphasized.

Keywords: Gradient Search, Increasing Revenue, Particle Filter Mechanism, Social Recommendation, Stemming Algorithm