VisualUrText: A Text Analytics Tool for Unstructured Textual Data

Zainol, Zuraini; Jaymes, Mohd T. Hamid; Nohuddin, Puteri N. E.

doi:10.1088/1742-6596/1018/1/012011

Cited by 24 publications

(17 citation statements)

References 6 publications

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“…Another study Arora and Badal [12], they applied Association Rule Mining in investigating academic performance of college students. This study will use the relationship regulation analysis technique [9,10,11] to look at the pattern of trends that exist in academic achievement among children of the B40 community.…”

Section: Background and Related Workmentioning

confidence: 99%

Study of B40 Schoolchildren Lifestyles and Academic Performance using Association Rule Mining

Nohuddin¹,

Zainol²,

Hijazi³

2021

AETiC

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B40 community school children experience many underprivileged lifestyles which impacted their academic performance. Through data trends and patterns, the education top management can observe the progress of academic performance and the lifestyle relationships of students in a school or nearby school. It can also help to identify the causes of progress or deterioration of the performance of B40 community students. Therefore, a data analytics framework is essential to help decision-makers to see and analyze the changing trends and patterns of academic progress in data and related lifestyles of B40 community students more effectively and accurately. The objective of this study is to design and develop association rules analysis to deduce the relevance of academic achievement and lifestyle among schoolchildren from the B40 family. The analysis framework is established in several stages that involve data collection and processing and transformation, then the design, application and evaluate of the association rules algorithms. The framework is expected to benefit students, teachers and Education Ministry. This study foresees whether educational programs and healthy lifestyle awareness can be designed specifically for the B40 children so as to improve their academic achievement as desired by the government.

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Section: Background and Related Workmentioning

confidence: 99%

Study of B40 Schoolchildren Lifestyles and Academic Performance using Association Rule Mining

Nohuddin¹,

Zainol²,

Hijazi³

2021

AETiC

Self Cite

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“…Data available in the text form has a great importance, and text mining has recently become quite prominent, especially for SNA 63 . Text mining is a multidisciplinary field, involving text analysis, information extraction, information retrieval, computational linguistics, categorization, clustering, visualization, database technology, and machine learning, and also, it can be viewed as an extension of data mining 64,65 . It should be noted that text mining is more complex than data mining because it handles unstructured, irregular, and fuzzy data patterns, whereas data mining is more likely to deal with structural datasets 66 .…”

Section: Fundamentals Of Snamentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

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“…According to [27], Text Mining (TM) can be defined as an analytic process that is designed to explore the unstructured text documents in search of useful information and knowledge hidden from a large amount of text resources. TM has been widely applied in many application domains such as military knowledge [28][29][30][31], business analytics [32][33][34], documents analysis [35][36][37], social media issues [38][39][40], etc. In this research, TM which is one of DM method is explored as to find new knowledge through examining qualitative data of patient experiences while having chemo treatment [20,21].…”

Section: Background and Related Workmentioning

confidence: 99%

Relationship Analysis on the Experience of Hospitalised Paediatric Cancer Patient in Malaysia using Text Analytics Approach

Zainol

Nohuddin²,

Rasid

et al. 2019

IJACSA

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The purpose of this study is to analyse the keyword relationships of paediatric cancer patient's experiences whilst being hospitalised during the treatment session. This study collects data through 40 days of observations on 21 paediatric cancer patients. A combination of text analytical visualizations such as network analysis map and bubble graph to analyse the data is applied in this study. Through the analysis, keywords such as "cri" (crying), "lay" (laying), "sleep" (sleeping) and "watch" (watching) are found the most common activities that have been experienced by paediatric cancer patients when they were hospitalised. Based on this observation, it can be argued that these activities can be represented as the experience that they have whilst being in the hospital. Based on the findings, hospitalised paediatric cancer patient's experience is limited due to the treatment protocol that requires them to be attached to intravenous line. Therefore, most of their activities are focused in bed such as sleeping, playing with their mobile, watching video and so on in bed. This study also offers a novel approach of transforming cancer patient data into useful knowledge about keyword relationship in paediatric cancer patient's experience during their stay in the hospital. The incorporation of these two text analytics offers insights for researchers to understand the interesting hidden knowledge in the collection of unstructured data, and this information can be used by medical providers, psychologists, games designers and others to develop any applications that can assist their difficulties and ease their pain while warded in the hospital.

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VisualUrText: A Text Analytics Tool for Unstructured Textual Data

Cited by 24 publications

References 6 publications

Study of B40 Schoolchildren Lifestyles and Academic Performance using Association Rule Mining

Study of B40 Schoolchildren Lifestyles and Academic Performance using Association Rule Mining

Semantic analysis on social networks: A survey

Relationship Analysis on the Experience of Hospitalised Paediatric Cancer Patient in Malaysia using Text Analytics Approach

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