scholarly journals Sentiment Analysis- A tool for Data Mining in Big Data Analytics

2019 ◽  
Vol 8 (9) ◽  
pp. 2125-2131
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Secondary Data ◽  
Business Environment ◽  
Analysis Tool ◽  
Business World ◽  
Rapid Changes ◽  
Abundant Data

With the current business environment and rapid changes in technology, the amount of data produced is increasing as each day passes. This huge collection of data is what can make or break such institutions, so it is vital for such a sector to efficiently utilize the data generated. Effective tools and analyses are required to make sure that this data is comprehended and organized in such a manner that it can be used for the tasks at hand. The challenge faced here is knowing how to extract and use the data to the benefit of the business world. The objective of understanding the underlying emotion displayed in each opinion that is voiced out is a huge exercise. Through this paper an attempt has been made to understand how the gap between consumers and providers can be bridged by analyzing secondary data through Sentiment Analysis tool. This research proposes a framework CSA (Continuous Sentiment Analysis) to repeatedly analyze the sentiments from customers highlighting the purpose of one such attempt to capture the tone of the message. This method of “Sentiment Analysis”- a fairly new field uses Natural Language Processing (NLP) in order to give meaning to the abundant data available at hand.

scholarly journals Data Lake Ecosystem Workflow

2021 ◽  
Author(s):  
R. Salter ◽  
Quyen Dong ◽  
Cody Coleman ◽  
Maria Seale ◽  
Alicia Ruvinsky ◽  
...  
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Lake Ecosystem ◽  
Data Governance ◽  
Government Organizations ◽  
Large Scale Data ◽  
Scale Data

The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.

scholarly journals An Intelligent Cognitive-Inspired Computing with Big Data Analytics Framework for Sentiment Analysis and Classification

2022 ◽  
Vol 59 (1) ◽  
pp. 102758
Author(s):  
Deepak Kumar Jain ◽  
Prasanthi Boyapati ◽  
J. Venkatesh ◽  
M. Prakash
Keyword(s):  
Big Data ◽  
Sentiment Analysis ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals The Potential Use of FinTech Developments in Takaful

2021 ◽  
Vol 8 (2) ◽  
pp. 109-121
Author(s):  
Hafidh Abdulla Hemed ◽  
Arwa Abubaker Abdullah Alamoudi ◽  
Anas Abdulkadir Abubakar Al Qassim ◽  
Bandar Mohammed Saif Qasem
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Insurance Industry ◽  
Academic Research ◽  
Big Data Analytics ◽  
Customer Experience ◽  
Business Environment ◽  
Competitive Intelligence ◽  
Islamic Insurance ◽  
Potential Use

Despite the increasingly important role that fintech play in the takaful industry, academic research in this area is quite limited. The overall aim of this paper it thus to explore the potential use of fintech in the Islamic insurance industry, especially in terms of its opportunities and challenges. Specifically, big data analytics and robo-advisory were explored and how takaful operators might incorporate them for better customer experience and gathering competitive intelligence. To remain competitive in a fast changing business environment, takaful operators need to identify and adopt fintech that could influence positively customer experience and optimise cost efficiency. This paper reviews the literature on big data analytics and robo-advisory, aiming to shed the light on the barriers and benefits of harnessing these technological advancements for takaful operators.

scholarly journals Relevance of Big Data for Business and Management. Exploratory Insights (Part I)

2018 ◽  
Vol 13 (2) ◽  
pp. 153-163
Author(s):  
Claudia Ogrean
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Secondary Data ◽  
Supply Side ◽  
Critical Content ◽  
History Of ◽  
The Impact ◽  
Business And Management ◽  
Ubiquitous Presence

AbstractOver the last few decades Big Data has impetuously penetrated almost every domain of human interest/action and it has (more or less consciously) become a ubiquitous presence of day to day life. The main questions this exploratory paper seeks to address (throughout its two parts) are the following: What is the (actual) impact of Big Data on Business & Management and How can businesses (through their management) leverage the potential of Big Data to their benefit? A gradual, step by step approach (based on literature review and a variety of secondary data) will guide the paper in search for answers to the abovementioned questions: starting with a concise history of the topic Big Data as reflected in academia and a critical content analysis of the Big Data concept, the paper will then continue by emphasizing some of the most significant realities and trends that characterize the supply-side of the big data industry; the second part of the paper is dedicated to the investigation of the demand-side of the big data industry – by highlighting some evidences (and projections) on the impact of big data analytics on Business & Management (both at aggregate and granular level) and exploring what companies could and should do (through their management) in order to best capitalize on the opportunities of big data and avoid/minimize the impact of its threats.

Learning Organisation

2021 ◽  
pp. 2570-2591
Author(s):  
Chandra Sekhar Patro
Keyword(s):  
Business Environment ◽  
Organisational Performance ◽  
Learning Organisation ◽  
New Techniques ◽  
Business World ◽  
Rapid Changes ◽  
New Ideas ◽  
To Come ◽  
Changing Demographics ◽  
Market Learning

In an ever-changing business world, the need to gain a competitive advantage has become extremely imperative for enterprises to survive in the age of globalisation. Much emphasis on learning has arisen due to rapid changes in the business environment, including uncertain market conditions, increasing complexity, changing demographics, and global competition. The companies are forced to innovate and develop new techniques for improving the quality and functionality of products, reduce costs, and respond to the highly elegant customers' demands in order to survive in the market. Learning organisations encourage the groups to come together and explore new ideas without being directed by a manager. The main objective of the chapter is to measure the effect of the dimensions of the learning organisation on organisational performance. A positive statistical relationship exists among the learning organisation dimensions and with organisational performance. Organisations must consider the learning organisation dimensions to enhance employees efficiency and organisational performance.

Data Management and Big Data Analytics

2019 ◽  
pp. 1-23
Author(s):  
Vellingiri Jayagopal ◽  
Basser K. K.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Management ◽  
Sentiment Analysis ◽  
Data Analytics ◽  
Digital Images ◽  
Big Data Analytics ◽  
Database Systems ◽  
The Internet ◽  
Processing Capacity

The internet is creating 2.5 quintillion bytes of data, and according to the statistics, the percentage of data that has been generated from last two years is 90%. This data comes from many industries like climate information, social media sites, digital images and videos, and purchase transactions. This data is big data. Big data is the data that exceeds storage and processing capacity of conventional database systems. Data in today's world (big data) is usually unstructured and qualitative in nature and can be used for various applications like sentiment analysis, increasing business, etc. About 80% of data captured today is unstructured. All this data is also big data.

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