No abstract

Modern society has a great influence on social networks which have been used to share user’s opinions and ideologies. Opinions discussed in social media about any emergency public event happenings. However, analyzing the opinion proliferation, producing interesting facts, which helps to enhance public security in emergencies. A lot of approaches are available to analyze the problem but suffer to achieve higher performance. This paper presents a real-time opinion prediction method. It analyzes the influence or hit rate of opinion in any case. This method first generates the network with several nodes where each user has been considered as a node. With the trace of social chat, the method classifies and groups the users under different categories of interest. The interest detection is performed according to the Class Level Post Measure (CLPM) which represents the interest of the user under a specific category. Using the actors identified, the method generates an Opinion Hit Matrix (OHM) based on the events and opinions posted. Using the matrix, the method computes the opinion support measure (OSM) to select a subset of opinions to generate recommendations. The proposed algorithm improves the performance of the recommendation generation.

Information proliferation and opinion analysis in social networks play a vital role to attract the public and government views. Most of the previous works in the literature have discussed information proliferation and opinion analysis separately. In this paper, a new methodology, for analysing both diffusion evolution and opinion dynamics is proposed. The proposed model is built using a forest fire algorithm, cuckoo search and fuzzy c-means clustering. The nature-inspired forest fire algorithm is used to determine the diffuser and non-diffuser of the information in the social networks and the time-stamp based fuzzy c-means clustering with the cuckoo search optimization algorithm is proposed to classify the content of the social network namely twitter into various opinions categories and to determine the opinion dynamics for time. The comparison of results on the different tweets data sets shows that the proposed model could improve the opinion dynamics prediction performance with the measurements namely precision, recall and accuracy than the other existing models.

Social media plays an important role in disseminating information and analysing public and government opinions. The vast majority of previous research has examined information diffusion and opinion analysis separately. This study proposes a new framework for analysing both information diffusion and opinion evolution. The change in opinion over time is known as opinion evolution. To propose a new model for predicting information diffusion and opinion analysis in social media, a forest fire algorithm, cuckoo search, and fuzzy c-means clustering are used. The forest fire algorithm is used to determine the diffuser and non-diffuser of information in social networks, and fuzzy c-means clustering with the cuckoo search optimization algorithm is proposed to cluster Twitter content into various opinion categories and to determine opinion change. On different Twitter data sets, the proposed model outperformed the existing methods in terms of precision, recall, and accuracy.