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Mining Publication Papers via Text Mining: A Case Study

  • Ahmed S. IbrahimEmail author
  • Sally Saad
  • Mostafa Aref
Conference paper
  • 161 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

The amount of data that produced is increased day after day especially data as a text, so with this massive production it would be difficult to analyze or extract information to discover the patterns from the unstructured text. Text mining is used for availing the massive amount of knowledge that is in the text and deriving high quality information from the text automatically. This Process would save effort and time. Text mining considered as a subset of data mining where data mining is more generic. This paper proposes a methodology of mining a text for a case study related to publication papers. Some of text mining approaches will be introduced for mining the publication papers using machine learning (ML) and natural language processing (NLP) techniques. Describing each phase as following: First phase is keywords extraction using natural language processing techniques, second phase named entity recognition and last phase is document classification. The last two phases are using the ML techniques. Then a case study is built to simulate the system phases, showing what is the input and the output in each phase.

Keywords

Text mining Data mining Named Entity Recognition (NER) Mining publication papers Machine Learning (ML) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer and Information SciencesAin Shams UniversityCairoEgypt

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