The purpose of this study is to conduct a bibliometric analysis to examine the most influential journals, institutions, and countries in social media (SM) publications related to knowledge management (KM). Moreover, various research themes in SM KM publications are also explored. VOSviewer was employed to process 234 SM KM publications retrieved from Web of Science (WoS) in the time period 2009-2019. Different methodologies were used according to the nature of bibliometric analysis and explained in each section. Journal of Knowledge Management was the most influential journal in SM KM publications. USA and England ranked first and second respectively, while the Tampere University of Technology was the most productive institute in SM KM research. Four emerged themes indicated an explicit contribution of SM users in KM through big data, knowledge sharing, innovation, Enterprise 2.0, and social capital. This is the first bibliometric study that explores the overall contribution of SM publications in the KM field.

Bibliometric Analysis of Social Media as a Platform for Knowledge Management

The pervasiveness of Social Media and user-generated content has triggered an exponential increase in global data volumes. However, due to collection and extraction challenges, data in many feeds, embedded comments, reviews and testimonials are inaccessible as a generic data source. This paper incorporates Knowledge Management framework as a paradigm for knowledge management and data value extraction. This framework embodies solutions to unlock the potential of UGC as a rich, real-time data source for analytical applications. The contributions described in this paper are threefold. Firstly, a method for automatically navigating pagination systems to expose UGC for collection is presented. This is evaluated using browser emulation integrated with dynamic data collection. Secondly, a new method for collecting social data without any a priori knowledge of the sites is introduced. Finally, a new testbed is developed to reflect the current state of internet sites and shared publicly to encourage future research. The discussion benchmarks the new algorithm alongside existing data extraction techniques and provides evidence of the increased amount of UGC data made accessible by the new algorithm.

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Unlocking Social Media and User Generated Content as a Data Source for Knowledge Management

The purpose of this research is to investigate the emerging scientific themes in business analytics through the utilisation of burst detection, text-clustering and word occurrence analysis in top information systems journals in order to provide an insight about the future scientific trends of business analytics for scholars and practitioners in the field. Researchers have gathered a rich set of business analytics articles from top journals which are indexed in the well-known scientific database of web of science (WoS) core collection. The study provides clues, directions, and knowledge-based guidelines on the recent business analytics scientific trends through the utilisation of mentioned algorithms over paper abstracts, titles, and keywords. This study also highlights the most important areas of research and the future research directions that might be interesting to business analysts through an in-depth analytical discussion.

A comparative analysis of emerging scientific themes in business analytics

Sentiment analysis has been applied to the datasets collected from social networking websites to get valuable insights. The exemplary growth of social networking has attracted researchers, and there has been a vast contribution in this area. In this chapter, we discuss the basics of sentiment analysis and its methodology, including data collection, data pre-processing, and feature extraction methods. Next, we discuss enhancement techniques for sentiment analysis, including text categorization, feature selection, data integration, ontology-based approaches, and so on. The chapter further provides information about available sentiment analysis tools, challenges, and evaluation metrics. We also discuss sentiment analysis applications and insights for further attention.

Introduction to Sentiment Analysis Covering Basics, Tools, Evaluation Metrics, Challenges, and Applications

Text mining in English language has been researched extensively in past and significant amount of resources, tools and techniques have been developed. India is a country of high language diversity. A large amount of textual data is available in Indian languages. Knowledge can be discovered from this text by applying text-mining techniques. Due to the characteristics of Indian languages, tools, techniques and resources available for mining text in English language cannot be applied directly to text in Indian languages. We could not find any comprehensive literature describing the research work related to mining of text written in Indian languages. In this paper, we review the research work done so far, availability of language resources and various challenges of text mining tasks in Indian languages.

A review of recent advances in text mining of Indian languages

This﻿article﻿describes﻿how﻿knowledge﻿discovery﻿is﻿a﻿frontier﻿research﻿issue﻿of﻿knowledge﻿management,﻿ and﻿social﻿media﻿provides﻿an﻿opportunity﻿for﻿knowledge﻿discovery﻿that﻿was﻿at﻿no﻿other﻿time﻿as﻿virtuous﻿ as﻿the﻿present.﻿Despite﻿the﻿fact﻿that,﻿the﻿articulations﻿in﻿national﻿dialects﻿via﻿web-based﻿networking﻿ media﻿is﻿mounting﻿day﻿by﻿day.﻿This﻿discovery﻿endeavor﻿in﻿regional﻿languages﻿is﻿rare.﻿The﻿usage﻿of﻿ Hindi,﻿the﻿Indian﻿National﻿language,﻿is﻿also﻿observing﻿the﻿similar﻿trend.﻿Any﻿expression﻿in﻿social﻿ media﻿ contains﻿ multiple﻿ features.﻿ Discovering﻿ the﻿ hidden﻿ sentiments﻿ behind﻿ these﻿ features﻿ have﻿ wider﻿functions.﻿This﻿article﻿is﻿the﻿first﻿attempt﻿to﻿mine﻿the﻿opinion﻿at﻿the﻿features﻿level﻿in﻿the﻿Hindi﻿ language.﻿The﻿principle﻿contribution﻿of﻿this﻿article﻿is﻿the﻿development﻿of﻿context﻿specific﻿corpus﻿ in﻿the﻿Hindi﻿language.﻿Based﻿on﻿this﻿corpus﻿authors﻿extract﻿the﻿sentiments﻿on﻿one﻿of﻿the﻿prominent﻿ leader﻿of﻿India﻿at﻿the﻿feature﻿level.﻿Opinion﻿mining﻿conclusion﻿based﻿on﻿present﻿work﻿is﻿reproduced﻿ likewise﻿in﻿the﻿subsequent﻿election﻿results. KEywoRDS Blog Reviews, Feature Level Opinion Mining, Hindi Language, Information Search and Retrieval, Natural Languages Processing, Test Mining

Knowledge Discovery From Vernacular Expressions: An Application of Social Media and Sentiment Mining

Information and Communication Technology (ICT) is revolutionizing many sectors but health sector is lagging in adopting ICT. In the manual system, much of the data is difficult to access and is not available in real time. There is lack of coordination between clinical service providers (doctor, nurse, patient and management) and other services such as pharmacy, procurement, laboratory and radiology. Digitization of hospital is vital in providing quality and cost-effective services to patients and improving support services. The increased digital adoption in the healthcare sector today has brought in major breakthroughs in information sharing, research, treatment methods, and communication models of the healthcare providers. However, the use of digitization in hospitals is not well understood and well structured. There is a need for common framework which will support all stakeholders of the hospital. This paper attempts to provide a framework for digitization of hospital operations and services.

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Digitization of Hospital Services and Operations: A Conceptual Framework

Investigations on sentiment mining are mostly ensued in the English language. Due to the characteristics of the Indian languages tools and techniques used for sentiment mining in the English langua...

Nishikant Bele

Papers

A review of recent advances in text mining of Indian languages

Knowledge Discovery From Vernacular Expressions: An Application of Social Media and Sentiment Mining

Digitization of Hospital Services and Operations: A Conceptual Framework

Political Sentiment Mining: A New Age Intelligence Tool for Business Strategy Formulation