The societal communication of the Q&A community on topic modeling

The emerging trend in technical research is to use client-generated data composed of community media to survey community opinion and scientific communication on employment and care issues. This study gathered data from the social website questions and answers and the indirect effects of stack to survey the key factors that influence public preferences in technical knowledge and opinions. Using a web search engine, topic modeling, and regression data modeling, this study quantitatively analyzed the effects of the textual and auxiliary functions of the response on the number of votes received with the response. Compared to previous surveys based on open assessments, the model results show that Quora users are more likely to talk only about technology. It may fail when query keywords do not match the text content of large documents containing relevant questions from the existing methods. Furthermore, consumers are generally not experts and provide ambiguous queries (Q&As) that lead to mixed results and face a problem with existing methods. To solve these problems, the researchers attempted to reorganize the primary results and present advanced distributed topic modeling techniques to address technologies and platforms by increasing the attributes and the time and space required to generate the model. This work briefly describes the public question-and-answer structure around the world and follows the development of the main themes of housing and employment opportunities for next-generation technologies worldwide in real-time scrolling.