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Learning Contextual Knowledge Structures from the Web for Facilitating Semantic Interpretation of Tweets

  • Nazura JavedEmail author
  • Muralidhara B. L.
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)

Abstract

Tweet analysis can provide valuable insight into societal issues and opinions. The terse, cryptic tweets, however, cannot be interpreted on face value. Interpretation assumes contextual knowledge. We propose a novel methodology of extracting structured contextual knowledge for popular topics/events and building knowledge structures using mining and computational linguistics techniques. We crunch relevant context contents from online sources and structure the same as contextual knowledge structures (CKSs). These automatically extracted CKS are (a) structured as subject–predicate–object triples, (b) they are relevant because they are built by mining contextual Web content, and (c) they are scalable to ontology and can be used for training classifiers. We demonstrate the feasibility and effectiveness of this methodology with an experiment which captures tweets of Indian political leaders, taps the related Web content, and transforms the same into CKS. The novel contribution of this work is its synergistic approach which combines acquisition, organization, and summarization with scalability to contextual ontology for social media analytics.

Keywords

Social media mining Contextual knowledge structures Text mining Computational linguistics Machine learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and ApplicationsBangalore UniversityBengaluruIndia

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