scholarly journals A Text Mining Research Based on LDA Topic Modelling

2016 ◽  
Author(s):  
Zhou Tong ◽  
Haiyi Zhang
Keyword(s):  
Text Mining ◽  
Topic Modelling

scholarly journals A Text Mining Based Map of Engineering Design: Topics and their Trajectories Over Time

2019 ◽  
Vol 1 (1) ◽  
pp. 2765-2774 ◽  
Author(s):  
Filippo Chiarello ◽  
Nicola Melluso ◽  
Andrea Bonaccorsi ◽  
Gualtiero Fantoni
Keyword(s):  
Text Mining ◽  
Engineering Design ◽  
State Of The Art ◽  
Topic Modelling ◽  
Specific Topic ◽  
Scientific Publications ◽  
New Knowledge ◽  
New Research ◽  
Large Corpus ◽  
Over Time

AbstractThe Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.This leads to complications in the researchers’ aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.

scholarly journals A text mining and topic modelling perspective of ethnic marketing research

2019 ◽  
Vol 103 ◽  
pp. 275-285 ◽  
Author(s):  
Sérgio Moro ◽  
Guilherme Pires ◽  
Paulo Rita ◽  
Paulo Cortez
Keyword(s):  
Text Mining ◽  
Marketing Research ◽  
Topic Modelling ◽  
Ethnic Marketing

scholarly journals Challenging suicide, burnout, and depression among veterinary practitioners and students: text mining and topics modelling analysis of the scientific literature

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Marta Brscic ◽  
Barbara Contiero ◽  
Alessandro Schianchi ◽  
Cristina Marogna
Keyword(s):  
Text Mining ◽  
Psychological Health ◽  
Scientific Literature ◽  
Topic Modelling ◽  
Veterinary Students ◽  
Staff Members ◽  
Preventive Actions ◽  
Modelling Analysis ◽  
Work Family ◽  
Set Up

Abstract Background Worldwide, veterinary practitioners and students are reported to be at higher risk of suicide, burnout, and depression compared to other occupational groups. The aim of the current study was to apply text mining and topic modelling analysis on scientific literature regarding suicide, burnout, and depression among veterinary practitioners and students to extract meaningful and synthetic information. These statistical approaches can be used to comprehend more in deep the phenomena involving veterinarians and veterinary students and to suggest the potential changes needed in admission to veterinary school, veterinary curricula, and post-graduation initiatives as preventive actions. Results A systematic search protocol was set up to identify scientific literature that published on the topic from 1985 to 2019. Two-hundred-eleven records were selected with abstracts/texts submitted to text mining and topic modelling analysis. Student, stress, work, anim*, and euthanasia resulted the most frequent terms. Topics modelling allowed to differentiate groups of words and papers in 3 areas of interest: 1) students’ difficulties encountered during their studies that increase stress and anxiety impairing their psychological health; 2) exposure to death and euthanasia as risk factor for mental health; and 3) need of support among those providing medical and health care, and of supportive group work to cope with such profession. Conclusion Based on the most frequent words included in the clouds and on the contents of the papers clusterised in them, some suggestions are interfered. It is emphasized that the veterinary curricula should include courses that prepare them early to deal with animal death and post-death grief of pet owners, to handle ethical dilemmas and moral stressors, to communicate with clients and staff members, to work in team, to balance work-family life and to promote individual and team resources. Specific courses for veterinary practitioners could keep them updated on their new roles and ways to handle them among functioning as potential feedbacks to monitor their psychological wellbeing.

Zakat administration in times of COVID-19 pandemic in Indonesia: a knowledge discovery via text mining

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fahmi Ali Hudaefi ◽  
Rezzy Eko Caraka ◽  
Hairunnizam Wahid
Keyword(s):  
Text Mining ◽  
Virtual Environment ◽  
Latent Dirichlet Allocation ◽  
Single Case ◽  
Critical Role ◽  
Vital Role ◽  
Topic Modelling ◽  
Content Type ◽  
National Board ◽  
Learning Analysis

Purpose Zakat during the COVID-19 outbreak has played a vital role and has been significantly discussed in the virtual environment. Such information about zakat in the virtual world creates unstructured data, which contains important information and knowledge. This paper aims to discover knowledge related to zakat administration during the pandemic from the information in a virtual environment. Furthermore, the discussion is contextualised to the socio-economic debates. Design/methodology/approach This is a qualitative study operated via text mining to discover knowledge of zakat administration during the COVID-19 pandemic. The National Board of Zakat Republic of Indonesia (BAZNAS RI) is selected for a single case study. This paper samples BAZNAS RI’s situation report on COVID-19 from its virtual website. The data consists of 40 digital pages containing 19,812 characters, 3,004 words and 3,003 white spaces. The text mining analytical steps are performed via RStudio. The following R packages, networkD3, igraph, ggraph and ggplot2 are used to run the Latent Dirichlet Allocation (LDA) for topic modelling. Findings The machine learning analysis via RStudio results in the 16 topics associated with the 3 primary topics (i.e. Education, Sadaqah and Health Services). The topic modelling discovers knowledge about BAZNAS RI’s assistance for COVID-19 relief, which may help the readers understand zakat administration in times of the pandemic from BAZNAS RI’s virtual website. This finding may draw the theory of socio-economic zakat, which explains that zakat as a religious obligation plays a critical role in shaping a Muslim community's social and economic processes, notably during the unprecedented times of COVID-19. Research limitations/implications This study uses data from a single zakat institution. Thus, the generalisation of the finding is limited to the sampled institution. Practical implications This research is both theoretically and practically important for academics and industry professionals. This paper contributes to the novelty in performing text mining via R in gaining knowledge about the recent zakat administration from a virtual website. The finding of this study (i.e. the topic modelling) is practically essential for zakat stakeholders to understand the contribution of zakat in managing the COVID-19 impacts. Social implications This work derives a theory of “socio-economic zakat” that explains the importance of a zakat institution in activating zakat for managing socio-economic issues during the pandemic. Thus, paying zakat to an authorised institution may actualise more maslahah (public interest) compared to paying it directly to the asnaf (zakat beneficiaries) without any measurement Originality/value This study is among the pioneers in gaining knowledge from Indonesia’s zakat management during the COVID-19 outbreak via text mining. The authors’ way of analysing data from the virtual website using RStudio can advance Islamic economics literature.

Understanding Peoples' Sentiment During Different Phases of COVID-19 Lockdown in India

2021 ◽  
Vol 8 (4) ◽  
pp. 52-68
Author(s):  
Rabindra Ku Jena ◽  
Rupashree Goswami
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Language Processing ◽  
Age Group ◽  
Topic Modelling ◽  
The Public ◽  
Global Pandemic ◽  
Gender And Age ◽  
The Face ◽  
And Control

During a global pandemic like COVID-19, the success of governmental policies depends on the people's sentiments and extended cooperation towards these policies. Therefore, this study explores the prevalent discourse in social media about different aspects of the COVID-19 pandemic and the policies to manage and control it. Data from Twitter collected between 25 March 2020 and 1 July 2020 was used for topic modelling and sentiment analysis. Natural language processing-based text mining techniques were used for analysis. This study first identified different frequent COVID-19-related topics and then analyzed how the sentiments towards these topics differ across different phases of lockdown. Further, insights into how different topics were perceived by gender and age group are also discussed in this study. Finally, this study also analyzed how daily casualty due to COVID-19 influenced the public sentiments and number of daily tweets. The study provides a robust NLP-based text mining framework to predict the people's sentiment during COVID-19 lockdown in India. The insights presented in this study can help the authorities mitigate the COVID-19 pandemic effectively and help different agencies in the face of similar pandemics in the future.

scholarly journals Text Mining Approach for Topic Modeling of Corpus Al Qur'an in Indonesian Translation

2018 ◽  
Author(s):  
Dwi Rolliawati ◽  
Indri Sudanawati Rozas ◽  
Khalid ◽  
Muhamad Ratodi
Keyword(s):  
Text Mining ◽  
Topic Modeling ◽  
Human Life ◽  
Point Of View ◽  
Topic Modelling ◽  
Word Count ◽  
Moderator Variables ◽  
Religious Text ◽  
Study Results ◽  
The World

The Qur’an is the religious text for Muslims that is revealed to humanity as a guide to solve any problems in all aspects of life. Therefore Quranic text is widely translated in various countries around the world, including in Indonesia which predominantly by Muslim. Difficulties in understanding the Quranic text in Arabic as well as the limited research on the Indonesian translation Quran related to science and technology, have opened a broad challenge to contribute to this realm. This paper proposed topic modelling of corpus in Indonesian Translation Quran by generated four main topics that are firmly related to human life, such as 1) heaven (surga) and hell (neraka), 2) The world (dunia) and the hereafter (akhirat), 3) Science (ilmu), charity (amal), and jihad, 4) Day (siang), night (malam), life (hidup), and death (mati). Those four topics were related to the moderator variables associated with the revelation location of Quranic verses (Makki and Madani). Of all the modeling topics tested by word count, Makki's Surahs contributes above 50% compared to Madani's Surahs. So the study results can be a reinforcement from the science's point of view that Makki verses were indeed emphasizing the faith as the foundation of Islam. This can be seen from the frequencies numbers that indicate the words “hidup” (161), “neraka” (157), “surga” (105), “dunia” (127), “amal” which is closely related to the human faith during their life in the world was discussed more in Makki's verses than Madani's.

Science and technology text mining:: Text Mining of the Journal Cortex

2013 ◽  
Author(s):  
Ronald N. Kostoff ◽  
◽  
Henry A. Buchtel ◽  
John Andrews ◽  
Kirstin M. Pfiel
Keyword(s):  
Text Mining ◽  
Science And Technology
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