Intelligent Strategy and Security in Education

2019 ◽  
pp. 87-110
Author(s):  
Samson Oluwaseun Fadiya
Keyword(s):  
Social Networking ◽  
Query Language ◽  
Unstructured Data ◽  
Text Analytics ◽  
Text Documents ◽  
Text Data ◽  
Web Based ◽  
Data Usage ◽  
Unstructured Text ◽  
Common Problems

Text analytics applies to most businesses, particularly education segments; for instance, if association or university is suspicious about data secrets being spilt to contenders by the workers, text analytics investigation can help dissect many employees' email messages. The massive volume of both organized and unstructured data principally started from the web-based social networking (media) and Web 2.0. The investigation (analysis) of messages online, tweets, and different types of unstructured text data constitute what we call text analytics, which has been developed during the most recent few years in a way that does not shift, through the upheaval of various algorithms and applications being utilized for the processing of data alongside the protection and IT security. This chapter plans to find common problems faced when using the different medium of data usage in education, one can analyze their information through the perform of sentiment analysis using text analytics by extracting useful information from text documents using IBM's annotation query language (AQL).

scholarly journals Text analysis on health product reviews using r approach

2020 ◽  
Vol 18 (3) ◽  
pp. 1303
Author(s):  
Nasibah Husna Mohd Kadir ◽  
Sharifah Aliman
Keyword(s):  
Language Processing ◽  
Text Summarization ◽  
Unstructured Data ◽  
Product Reviews ◽  
Health Product ◽  
Text Analytics ◽  
Data Set ◽  
Web Based ◽  
The Social ◽  
Key Techniques

In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. It combines all the NLP features in the menu part of the text analytics process in steps and it is labeled to make it easier for users to view all the text summarization. This study uses health product review from Shaklee as the data set. The proposed approach shows the acceptable performance in terms of system features execution compared with the baseline model system.

Incorporating Text OLAP in Business Intelligence

2011 ◽  
pp. 77-101 ◽  
Author(s):  
Byung-Kwon Park ◽  
Il-Yeol Song
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Business Intelligence ◽  
Multidimensional Analysis ◽  
Web Pages ◽  
Data Types ◽  
Text Documents ◽  
Text Data ◽  
Platform Architecture ◽  
Unstructured Text

As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both data types for total business intelligence. The data can be classified into two categories: structured and unstructured. For getting total business intelligence, it is important to seamlessly analyze both of them. Especially, as most of business data are unstructured text documents, including the Web pages in Internet, we need a Text OLAP solution to perform multidimensional analysis of text documents in the same way as structured relational data. We first survey the representative works selected for demonstrating how the technologies of text mining and information retrieval can be applied for multidimensional analysis of text documents, because they are major technologies handling text data. And then, we survey the representative works selected for demonstrating how we can associate and consolidate both unstructured text documents and structured relation data for obtaining total business intelligence. Finally, we present a future business intelligence platform architecture as well as related research topics. We expect the proposed total heterogeneous business intelligence architecture, which integrates information retrieval, text mining, and information extraction technologies all together, including relational OLAP technologies, would make a better platform toward total business intelligence.

scholarly journals Effective processing of unstructured data using python in Hadoop map reduce

2018 ◽  
Vol 7 (2.21) ◽  
pp. 417
Author(s):  
K Kousalya ◽  
Shaik Javed Parvez
Keyword(s):  
Open Source ◽  
Unstructured Data ◽  
Map Reduce ◽  
Text Data ◽  
Apache Hadoop ◽  
Unstructured Text ◽  
Wide Range ◽  
Two Stages

In present scenario, the growing data are naturally unstructured. In this case to handle the wide range of data, is difficult. The proposed paper is to process the unstructured text data effectively in Hadoop map reduce using Python. Apache Hadoop is an open source platform and it widely uses Map Reduce framework. Map Reduce is popular and effective for processing the unstructured data in parallel manner.  There are two stages in map reduce, namely transform and repository. Here the input splits into small blocks and worker node process individual blocks in parallel. This map reduce generally is based on java. While Hadoop Streaming allows writing mapper and reducer in other languages like Python. In this paper, we are going to show an alternative way of processing the growing unstructured content data by using python. We will also compare the performance between java based and non-java based programs. 

scholarly journals Challenges of combining structured and unstructured data in corpus development

2021 ◽  
Vol 9 (1) ◽  
pp. i-viii
Author(s):  
Tanja Säily ◽  
Jukka Tyrkkö
Keyword(s):  
Best Practices ◽  
Corpus Linguistics ◽  
Digital Humanities ◽  
Development Process ◽  
Unstructured Data ◽  
Special Issue ◽  
Unstructured Text ◽  
Recent Advances ◽  
Common Problems ◽  
Rich Data

Recent advances in the availability of ever larger and more varied electronic datasets, both historical and modern, provide unprecedented opportunities for corpus linguistics and the digital humanities. However, combining unstructured text with images, video, audio as well as structured metadata poses a variety of challenges to corpus compilers. This paper presents an overview of the topic to contextualise this special issue of Research in Corpus Linguistics. The aim of the special issue is to highlight some of the challenges faced and solutions developed in several recent and ongoing corpus projects. Rather than providing overall descriptions of corpora, each contributor discusses specific challenges they faced in the corpus development process, summarised in this paper. We hope that the special issue will benefit future corpus projects by providing solutions to common problems and by paving the way for new best practices for the compilation and development of rich-data corpora. We also hope that this collection of articles will help keep the conversation going on the theoretical and methodological challenges of corpus compilation.

scholarly journals LSA & LDA topic modeling classification: comparison study on e-books

2020 ◽  
Vol 19 (1) ◽  
pp. 353
Author(s):  
Shaymaa H. Mohammed ◽  
Salam Al-augby
Keyword(s):  
Digital Libraries ◽  
Full Text ◽  
Topic Modeling ◽  
Comparison Study ◽  
Text Documents ◽  
Text Data ◽  
Text Document ◽  
Unstructured Text ◽  
The One ◽  
Text Document Classification

<p>With the rapid growth of information technology, the amount of unstructured text data in digital libraries is rapidly increased and has become a big challenge in analyzing, organizing and how to classify text automatically in E-research repository to get the benefit from them is the cornerstone. The manual categorization of text documents requires a lot of financial, human resources for management. In order to get so, topic modeling are used to classify documents. This paper addresses a comparison study on scientific unstructured text document classification (e-books) based on the full text where applying the most popular topic modeling approach (LDA, LSA) to cluster the words into a set of topics as important keywords for classification. Our dataset consists of (300) books contain about 23 million words based on full text. In the used topic models (LSA, LDA) each word in the corpus of vocabulary is connected with one or more topics with a probability, as estimated by the model. Many (LDA, LSA) models were built with different values of coherence and pick the one that produces the highest coherence value. The result of this paper showed that LDA has better results than LSA and the best results obtained from the LDA method was (<strong>0.592179</strong>) of coherence value when the number of topics was <strong>20 while</strong> the LSA coherence value was <strong>(0.5773026)</strong> when the number of topics was 10.</p>

scholarly journals Extracting Value from Unstructured Data – Implementing Text Analytics on the Voice of Student

2020 ◽  
Vol 8 (4) ◽  
pp. 14-22
Author(s):  
Jiangping Wang
Keyword(s):  
Unstructured Data ◽  
Data Mining Algorithm ◽  
Text Analytics ◽  
Text Data ◽  
General Process ◽  
Business Operations ◽  
Building Models ◽  
Traditional Organization ◽  
The Voice

Unstructured data is chaotic and messy with little or no metadata and lacks of traditional organization structure. However, same as any structured data, unstructured data is also part of valuable business asset. Many times, it is text heavy and needs extensive preprocessing before data mining algorithm can apply for building models in order to reveal value hidden in the data. Text as a form of data is widely used in business operations as a major way of communication, generating increasing volumes of data. Text data in its raw form is relatively dirty. The embedded business value can be extracted through approaches in text mining and text analytics. This paper presents a case study in this general process of revealing value in unstructured data and applying on data collected to support online learning and student assistance.

Are social networking platforms the key to effective social distancing for COVID-19? (Preprint)

2020 ◽  
Author(s):  
Viknesh Sounderajah ◽  
Hutan Ashrafian ◽  
Sheraz Markar ◽  
Ara Darzi
Keyword(s):  
Operating System ◽  
Social Networking ◽  
Contact Tracing ◽  
Text Data ◽  
Social Distancing ◽  
Web Mapping ◽  
Viral Spread ◽  
Behavioural Insights ◽  
Mapping Software ◽  
Incidence And Mortality

UNSTRUCTURED If health systems are to effectively employ social distancing measures to in response to further COVID-19 peaks, they must adopt new behavioural metrics that can supplement traditional downstream measures, such as incidence and mortality. Access to mobile digital innovations may dynamically quantify compliance to social distancing (e.g. web mapping software) as well as establish personalised real-time contact tracing of viral spread (e.g. mobile operating system infrastructure through Google-Apple partnership). In particular, text data from social networking platforms can be mined for unique behavioural insights, such as symptom tracking and perception monitoring. Platforms, such as Twitter, have shown significant promise in tracking communicable pandemics. As such, it is critical that social networking companies collaborate with each other in order to (1) enrich the data that is available for analysis, (2) promote the creation of open access datasets for researchers and (3) cultivate relationships with governments in order to affect positive change.

Disaster Management during Pandemic: A Big Data-Centric Approach

2021 ◽  
pp. 2140003
Author(s):  
Mohamed Elsotouhy ◽  
Geetika Jain ◽  
Archana Shrivastava
Keyword(s):  
Big Data ◽  
Disaster Management ◽  
Crisis Response ◽  
Unstructured Data ◽  
Management Theory ◽  
Management Approach ◽  
Text Analytics ◽  
The Past ◽  
Structured Information ◽  
Data Files

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.

An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

scholarly journals Online social network size is reflected in human brain structure

2011 ◽  
Vol 279 (1732) ◽  
pp. 1327-1334 ◽  
Author(s):  
R. Kanai ◽  
B. Bahrami ◽  
R. Roylance ◽  
G. Rees
Keyword(s):  
Social Network ◽  
Social Networking ◽  
Real World ◽  
Brain Structure ◽  
Grey Matter ◽  
Online Social Network ◽  
Network Size ◽  
Matter Density ◽  
Web Based ◽  
Social Network Size

The increasing ubiquity of web-based social networking services is a striking feature of modern human society. The degree to which individuals participate in these networks varies substantially for reasons that are unclear. Here, we show a biological basis for such variability by demonstrating that quantitative variation in the number of friends an individual declares on a web-based social networking service reliably predicted grey matter density in the right superior temporal sulcus, left middle temporal gyrus and entorhinal cortex. Such regions have been previously implicated in social perception and associative memory, respectively. We further show that variability in the size of such online friendship networks was significantly correlated with the size of more intimate real-world social groups. However, the brain regions we identified were specifically associated with online social network size, whereas the grey matter density of the amygdala was correlated both with online and real-world social network sizes. Taken together, our findings demonstrate that the size of an individual's online social network is closely linked to focal brain structure implicated in social cognition.

Sign in / Sign up

Export Citation Format

Share Document