Abstract
The chapter delineates the conceptualization of deconstructive Big Data Analytics (DBDA) or studying semantic values of literary texts. In doing so, deconstruction is defined in terms of its developer Jacques Derrida’s philosophy of interpretation and its relationship with data analysis. Big Data is significantly a galaxy of networked data which requires its close reading to visualize its webbed constellations. Big Data Analysis demands the art of finding hidden meanings, disseminates each thread and fibers of data to let visualize the meanings and their contexts distinctively. When Big Data Analysis is used for experimental findings, being a bit different from what established findings are and that needs reverse study of data. The hypothesis of the chapter is that, deconstruction is used for bringing novelty and originality of results of each discovery, though it changes its nomenclatures in the existing world of knowledge. Relying on reviews of literature, researchers, and applications in social media, the authors explicate how deconstruction is practiced outside humanities and social sciences. In this context, application of deconstruction in science and technology would be studied focusing on how the application of deconstructive principles can benefit the knowledge of Big Data Analytics. Hence, the chapter would establish a novel theory of deconstructive big data analysis (DBDA), and feasible uses and functions of DBDA in the study of cognitive social media.
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Notes
- 1.
Valiur Rahaman’s coinage made of 'Derridean logos' and Bakhtin's heteroglossia. Doctoral Dissertation. “Implications of Literary Criticism in the Philosophy of Jacques Derrida”. 2010. MGKVP. Varanasi. India. The present chapter is one of the outcomes of TEQIP III-CRS project of which the corresponding author is PI.
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Rahaman, V., Haider, A. (2021). Deconstructive Big Data Analytics: Literary Texts Analysis Through Atlas.ti Software. In: Sharma, S., Rahaman, V., Sinha, G.R. (eds) Big Data Analytics in Cognitive Social Media and Literary Texts. Springer, Singapore. https://doi.org/10.1007/978-981-16-4729-1_3
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