An ontologybased user content analysis model for automated decision making

Abstract

Social media, as a data source, contains valuable consumer insights and it enables business intelligence. An enormous growth in the prevalence of social media platforms allow users to collaborate, communicate and share their newlineexperience with others; and these comments are trusted by the users. Hence, the newlinenecessity of annotating these documents has become important. However, most newlineof the unstructured text documents (comments, feedback) are not machine newlineunderstandable. Hence, it has to be modelled, analyzed, and visualized so as to newlinemake decisions easier. Document annotation with added semantics enables the newlineautomatic information or knowledge extraction from the repository. Social media text analytics on user-generated content, or sentiment analysis, is one of the applications of Information Extraction (IE). In recent years, there has been an increase in attention on social media as a source of research data, in areas such as Decision Making, Recommender Systems, and the like. newlineResearchers analyzed blogs and have showed its necessity in sharing of newlineexperience among people. Most of the research has been undertaken in newlinemarketing and retail sector to improve customer satisfaction, recommend new newlineproducts and so on. In some research, methods like context-based data newlinerepresentation rather than keyword-based approach were used. However, the newlineextraction of the emotional information from user reviews and comments is still newlinea research focus, as it requires effective text analytics techniques for text newlineprocessing newline newline