Real-Time Opinion Prediction Method for Emergency Public Events in Social Media Networks Using Opinion Hit Matrix

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.

Retracted: Public opinion as a means of managing social processes

“…The specific feature of public opinion and consciousness is that they not only reflect the social being but also affect its changes in a certain way. Analysing the current phenomenon, the authors of this study can conclude that it has a two‐faced character, which is expressed by its state (Uthirapathy & Sandanam, 2020). A complex of legal, political, and moral views that arise from the interaction of various ideas directly impact the opinion of society.…”

Aspect-Based Sentimental Analysis On Social Media Data Using Deep Learning Methods

“…Deep learning methods are essential for understanding complex data patterns in text and images, enabling accurate predictions and insights. For usual sentimental analysis, there are primarily five steps [2] namely data preparation, reviews evaluation, sentimental classification, and outcomes which relegates the reviews into three varieties: positive, negative, and neutral. This paper proposes a strategy to improve ASBA classification accuracy using an RNN method called LSTM with GCN.…”