Text Analytics and Big Data in the Financial domain

The concept of big data (BD) has been coupled with disaster management to improve the crisis response during pandemic and epidemic. BD has transformed every aspect and approach of handling the unorganized set of data files and converting the same into a piece of more structured information. The constant inflow of unstructured data shows the research lacuna, especially during a pandemic. This study is an effort to develop a pandemic disaster management approach based on BD. BD text analytics potential is immense in effective pandemic disaster management via visualization, explanation, and data analysis. To seize the understanding of using BD toward disaster management, we have taken a comprehensive approach in place of fragmented view by using BD text analytics approach to comprehend the various relationships about disaster management theory. The study’s findings indicate that it is essential to understand all the pandemic disaster management performed in the past and improve the future crisis response using BD. Though worldwide, all the communities face big chaos and have little help reaching a potential solution.

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Big Data and Text Analytics

The Emerald Handbook of Modern Information Management ◽

10.1108/978-1-78714-525-220171033 ◽

2017 ◽

pp. 791-809

Author(s):

Helen Clegg ◽

Hugo Evans

Keyword(s):

Big Data ◽

Text Analytics

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Text Analytics in Big Data Environments

Big Data Applications in Industry 4.0 ◽

10.1201/9781003175889-7 ◽

2021 ◽

pp. 125-143

Author(s):

R. Janani ◽

S. Vijayarani

Keyword(s):

Big Data ◽

Text Analytics

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Comprehensive Contemplation of Probabilistic Aspects in Intelligent Analytics

International Journal of Service Science Management Engineering and Technology ◽

10.4018/ijssmet.2020010108 ◽

2020 ◽

Vol 11 (1) ◽

pp. 116-141 ◽

Cited By ~ 2

Author(s):

Neeti Sangwan ◽

Vishal Bhatnagar

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Big Data Analysis ◽

Future Research ◽

Learning Approaches ◽

Text Analytics ◽

Review Of Literature ◽

Classification Framework

In Big Data analysis, the application of machine learning has proven to be a revolutionary. The systematic review of literature shows that research has been carried out on the domain of big data analytics particularly text analytics with the inclusion of machine learning approaches. This extensive survey deals with the data at hand that provides different ways and issues while combining the machine learning approaches with the text. During the course of the survey, various publications in the field of synchronous application of machine learning in text analytics were searched and studied. Classification framework is proposed as the contribution of machine learning in text analytics. A classification framework represented the various application areas to motivate researchers for future research on the application of two emerging technologies.

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What can big data and text analytics tell us about hotel guest experience and satisfaction?

International Journal of Hospitality Management ◽

10.1016/j.ijhm.2014.10.013 ◽

2015 ◽

Vol 44 ◽

pp. 120-130 ◽

Cited By ~ 317

Author(s):

Zheng Xiang ◽

Zvi Schwartz ◽

John H. Gerdes ◽

Muzaffer Uysal

Keyword(s):

Big Data ◽

Text Analytics

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Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.2019100101 ◽

2019 ◽

Vol 14 (4) ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

Viju Raghupathi ◽

Yilu Zhou ◽

Wullianallur Raghupathi

Keyword(s):

Big Data ◽

Data Analytics ◽

Word Association ◽

Big Data Analytics ◽

Text Analytics ◽

Hadoop Mapreduce ◽

Cancer Management ◽

Exploratory Approach ◽

Clustering And Classification ◽

Data Analytic

In this article, the authors explore the potential of a big data analytics approach to unstructured text analytics of cancer blogs. The application is developed using Cloudera platform's Hadoop MapReduce framework. It uses several text analytics algorithms, including word count, word association, clustering, and classification, to identify and analyze the patterns and keywords in cancer blog postings. This article establishes an exploratory approach to involving big data analytics methods in developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through various means, including the development of categories or keywords contained in the blogs, the development of a taxonomy, and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It can provide insight and decision support for cancer management and facilitate efficient and relevant searches for information related to cancer.

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