The current research program is data from social networks to analyze public opinion on technical terms or topics. Public perception of the existence of technical terms or themes and their impact on the environment and society is an important issue to the who are searching for job assistantship. Public assistance is also essential for legislation and the implementation of mitigation policies. The analysis of public opinion is essential for a better understanding of the social environment and social dynamics. Among the various sources of public opinion data, social media data attracts a lot of researchers' attention because it provides extremely valuable data on public attitudes and responses to conflicting socio-technical terms or problems from different perspectives, such as quorum, stack overflow, Yahoo! . It also reacts to Twitter, etc. and it is widely used to monitor and analyze society's reactions to a natural or social anomaly. Data on social media is generally collected by searching for keywords or a topic to find a number of topics in the topic templates. Sometimes users give an incorrect number of topics in traditional topic models, leading to poor grouping results. In such cases, correct visualizations are essential to retrieve information to identify trends in the cluster. In this regard, promising methods for modeling themes are related to unclassified and inaccurate texts or themes to overcome an existing problem. These methods are the Hadoop Latent Distributed Dirichlet Distribution (HdpLda), the Hadoop Latent Semantic Analysis (HdpLsa), and the Hadoop Distributed Nonnegative Matrix Factorization, and the comparative standard data is used in an experimental study to demonstrate the effectiveness of the models proposed in the grouping refinement (CA) and information (NMI), precision (P), recovery (R), measurement of F-Score (F) and computational complexity. This document briefly describes the public question-and-answer structure in the country and traces the development of major issues and initiatives with a particular focus on automatic dissemination of relevant customer responses and knowledge of relevant awareness-raising information you seek. Housing and job opportunities for the next generation technologies in global empowerment. Finally, based on the experimental results, topic models improve the precision more than the existing models for getting more relevant answers for placement and interview point view .
Volume 12 | Issue 6
Pages: 2012-2023
DOI: 10.5373/JARDCS/V12I2/S20201407