Relevant Word Determination from Dynamic Text Network

Abstract

Text network has been of research interest in the computational domain. A text network contains textual documents with dynamic content. The textual documents linguistically depend upon the grammar and use of words. We have proposed determining relevant words in dynamic content using dynamic complex network approaches. We considered dynamic news updates for exploring our analysis of text networks. This work proposes a complex network-based approach for determining the change of important words with dynamic content. We have suggested determining relevant words from dynamic content using degree centrality, closeness centrality and clustering coefficient. We have proposed Affiliation Factor for bond strength determination among the important words. We have considered an instance of live updates in the news for our approach. The news updates on a particular live incident are uploaded dynamically in a thread. We have introduced the Instance Factor for perceiving the relevance of the important words throughout the document, apart from considering the co-occurrence frequency. The approach is free of semantic analysis and, consequently, exempt depending on any corpus. The analytical results show that the varying important words can be efficiently determined from the dynamic content of the text network compared to the other approaches.

KEYWORDS:

• Centrality
• Complex networks
• Dynamic network
• Influential node
• Relevant word
• Text network

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Susmita Das

Susmita Das received the BTech degree in computer science and engineering from Haldia Institute of Technology, India. Subsequently she completed her MTech degree in computer science and technology from Indian Institute of Engineering Science and Technology, Shibpur, India. She is presently working as a PhD research scholar in IIEST, Shibpur, India. Her areas of interest are complex networks, community detection, etc. Email: [email protected].

Susanta Chakraborty

Susanta Chakraborty received the bachelor's and master's degrees in electrical and instrumentation engineering and the PhD (Tech) degree in computer science from the University of Calcutta, Kolkata, India, in 1983, 1985, and 1999, respectively. His PhD research was done at the Advance Computing and Microelectronic Unit, Indian Statistical Institute, Kolkata. He is currently Professor with the Department of Computer Science and Technology, and Dean (Academics) in Indian Institute of Engineering Science and Technology, Shibpur, India. He was a dean of the Engineering, Technology and Management Faculty, University of Kalyani, Kalyani, India. Dr Chakraborty received the INSA-JSPS Fellowship of the Indian National Science Academy in the session 2003-2004, and works have been done in collaboration with Prof H Fujiwara of the Nara Institute of Science and Technology, Japan. His areas of interest are social networks, big data analytics etc.

Samit Biswas

Samit Biswas received the BE degree in computer science and engineering from University of Burdwan, subsequently completed MTech degree in computer science and technology from Kalyani University and PhD degree in computer science and technology from IIEST Shibpur. He worked as lecturer in Computer science and engineering in BIT, Kolkata from 2006 to 2010 and as assistant professor in the same institute from 2010 to 2013. He is presently working as assistant professor in Indian Institute of Engineering Science and Technology, Shibpur, India. His areas of interest are image processing and analysis, pattern recognition etc. Email: [email protected].