Text classification algorithms for mining unstructured data: a SWOT analysis

It has become increasingly crucial and imperative to facilitate knowledge extraction for decision support and deliver targeted information to analysts that span wide application domains. Interestingly, the buzzing term “big data” which is estimated to be 90% unstructured further makes it difficult to tap and analyze information with traditional tools. Text mining entails defining a process which transforms and substitutes this unstructured data into a structured one to discover knowledge. Use of classification algorithms to intelligently mine text has been studied extensively across literature. This study predominantly surveys the text classification algorithms employed in the process of mining unstructured data to report a conclusive analysis on the trend of their use in terms of their respective strengths, weaknesses, opportunities and threats (SWOT). The scope of these algorithms is then explored apropos the application area of sentiment analysis, a typical text classification task. A mapping which determines the unexplored social media technologies and the extent of use of these algorithms within respective social media is proffered to give an insight to the amount of work that has been done in the domain of machine learning based sentiment analysis on social media. © 2018, Bharati Vidyapeeth's Institute of Computer Applications and Management.