Survey and Gap Analysis on Event Prediction of English Unstructured Texts
Published in Springer - 4th International Conference on ICT for Intelligent Systems (ICTIS 2020), 2020
Published in Springer : Lecture Notes in Networks and Systems (LNNS) Vol. 141, Amit Joshi et al: MACHINE LEARNING FOR PREDICTIVE ANALYSIS.
Abstract
In the age of Big Data Analytics, text mining on large sets of digital textual data does not suffice all the analytic purposes. Prediction on unstructured texts and interlinking of the information in various domains like strategic, political, medical, financial etc. is very pertinent to the users seeking analytics beyond retrieval. Along with analytics and pattern recognition from the textual data, there is a need to formulate this data and explore the possibility of predicting future event(s). Event Prediction can best be defined as the domain of predicting the occurrence of an event from the textual data. This paper spans over two main sections of surveying technical literature and existing tools/technologies functioning in the domain of prediction and analytics based on unstrucuted text. The survey also highlights fundamental research gaps from the reviewed literature. A systematic comparison of different technical approaches has been listed in a tabular form. The gap analysis provides future scope of more optimized algorithms for textual event prediction.
