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Top1. Introduction
Knowledge is the key asset of an organization (Gloet & Samson, 2016). In order to safeguard their interests and survive in the face of multidimensional pressure, organizations are showing keen interest in knowledge management (KM) (Carvalho & Gomes, 2017; Ryan, Zhang, Prybutok, & Sharp, 2012; Twum-Darko & Harker, 2017; Yesil & Hirlak, 2013). Knowledge discovery (KD) is the foremost component of KM (Smolnik, Kremer, & Kolbe, 2005) and includes information mapping, knowledge collaboration, disseminate learning, business insight, and knowledge security (Gold et al., 2001; Grant, 1996).
Scholarly writing is the primary source of KD. The English language overshadows the academic work of the world (Cavazos, 2016; Choi, 2010; Leppänen & Pahta, 2012) and it has been found that KD in native languages is almost disregarded. Text mining and sentiment mining are key components of KD for various organizations (Handzic, Lagumdzija, & Celjo, 2008) however, text mining and sentiment mining literature is also dominated by the English language (Harrag, 2014; Korayem, Aljadda, & Crandall, 2016; Vijayarani, Ilamathi, & Nithya, 2015).
Research points to a growing need to mine knowledge in native languages to gain contextual business insights. The aim of this study is to highlight the role of Hindi language in contextual knowledge discovery and to develop a framework to uncover sentiments from a large pool of textual data.
The rest of this paper is organized as follows: Section 2 comprises of literature review while Section 3 showcases the methodology used and our purposed framework. Section 4 describes the analysis and results of the study, and Section 5 discusses the implications, limitations, and future direction of research.