scholarly journals Text Mining in Organizational Research

2017 ◽  
Vol 21 (3) ◽  
pp. 733-765 ◽  
Author(s):  
Vladimer B. Kobayashi ◽  
Stefan T. Mol ◽  
Hannah A. Berkers ◽  
Gábor Kismihók ◽  
Deanne N. Den Hartog
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Topic Modeling ◽  
Analytical Techniques ◽  
Job Analysis ◽  
Organizational Research ◽  
Textual Data ◽  
Different Types ◽  
Research Questions ◽  
New Research

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.

Topic Modeling as a Strategy of Inquiry in Organizational Research: A Tutorial With an Application Example on Organizational Culture

2018 ◽  
Vol 22 (4) ◽  
pp. 941-968 ◽  
Author(s):  
Theresa Schmiedel ◽  
Oliver Müller ◽  
Jan vom Brocke
Keyword(s):  
Organizational Culture ◽  
Text Mining ◽  
Topic Modeling ◽  
Future Research ◽  
Data Sets ◽  
Organizational Research ◽  
Fortune 500 Companies ◽  
Textual Data ◽  
Complementary Strategy ◽  
Depth Interviews

Research has emphasized the limitations of qualitative and quantitative approaches to studying organizational phenomena. For example, in-depth interviews are resource-intensive, while questionnaires with closed-ended questions can only measure predefined constructs. With the recent availability of large textual data sets and increased computational power, text mining has become an attractive method that has the potential to mitigate some of these limitations. Thus, we suggest applying topic modeling, a specific text mining technique, as a new and complementary strategy of inquiry to study organizational phenomena. In particular, we outline the potentials of structural topic modeling for organizational research and provide a step-by-step tutorial on how to apply it. Our application example builds on 428,492 reviews of Fortune 500 companies from the online platform Glassdoor, on which employees can evaluate organizations. We demonstrate how structural topic models allow to inductively identify topics that matter to employees and quantify their relationship with employees’ perception of organizational culture. We discuss the advantages and limitations of topic modeling as a research method and outline how future research can apply the technique to study organizational phenomena.

scholarly journals Reusing Data Technical and Ethical Challenges

2019 ◽  
Vol 39 (06) ◽  
pp. 329-337
Author(s):  
Juan-José Boté ◽  
Miquel Termens
Keyword(s):  
Data Privacy ◽  
Data Reuse ◽  
Ethical Challenges ◽  
Different Types ◽  
Management Plans ◽  
Fair Principles ◽  
Research Questions ◽  
Public Organisations ◽  
New Research ◽  
Research Centres

Research centres, universities and public organisations create datasets that can be reused in research. Reusing data makes it possible to reproduce studies, generate new research questions and new knowledge, but it also gives rise to technical and ethical challenges. Part of these issues are repositories interoperability to accomplish FAIR principles or issues related to data privacy or anonymity. At the same time, funding institutions require that data management plans be submitted for grants, and research tends to be increasingly interdisciplinary. Interdisciplinarity may entail barriers for researchers to reuse data, such as a lack of skills to manipulate data, given that each discipline generates different types of data in different technical formats, often non-standardized. Additionally, the use of standards to validate data reuse and better metadata to find appropriate datasets seem necessary. This paper offers a review of the literature that addresses data reuse in terms of technical, ethical-related issues.

scholarly journals The Quality of Political Information

2020 ◽  
pp. 147892992091761
Author(s):  
Konstantin Vössing
Keyword(s):  
Empirical Research ◽  
Information Quality ◽  
Political Information ◽  
Political Messages ◽  
Two Component ◽  
Different Types ◽  
Research Questions ◽  
New Research

The article conceptualizes the quality of political information and shows how the concept can be used for empirical research. I distinguish three aspects of quality ( intelligibility, relevance, and validity) and use them to judge the constituent foundations of political information, that is, component claims (statements of alleged facts) and connection claims (argumentative statements created by causally linking two component claims). The resulting conceptual map thus entails six manifestations of information quality ( component claim intelligibility, connection claim intelligibility, component claim relevance, connection claim relevance, component claim validity, and connection claim validity). I explain how the conceptual map can be used to make sense of the eclectic variety of existing research, and how it can advance new empirical research, as a guide for determining variation in information quality, as a conceptual template for the analysis of different types of political messages and their common quality deficiencies, and as a generator of new research questions and theoretical expectations.

scholarly journals Diary Studies in Organizational Research

2010 ◽  
Vol 9 (2) ◽  
pp. 79-93 ◽  
Author(s):  
Sandra Ohly ◽  
Sabine Sonnentag ◽  
Cornelia Niessen ◽  
Dieter Zapf
Keyword(s):  
Missing Data ◽  
Data Collection ◽  
Diary Study ◽  
Methodological Issues ◽  
Short Term ◽  
Organizational Research ◽  
Diary Studies ◽  
Different Types ◽  
Research Questions ◽  
Practical Recommendations

In recent years, researchers in work and organizational psychology have increasingly become interested in short-term processes and everyday experiences of working individuals. Diaries provide the necessary means to examine these processes. Although diary studies have become more popular in recent years, researchers not familiar with this method still find it difficult to get access to the required knowledge. In this paper, we provide an introduction to this method of data collection. Using two diary study examples, we discuss methodological issues researchers face when planning a diary study, examine recent methodological developments, and give practical recommendations. Topics covered include different types of diary studies, the research questions to be examined, compliance and the issue of missing data, sample size, and issues of analyses.

scholarly journals The Evolution of Topic Modeling

2022 ◽  
Author(s):  
Rob Churchill ◽  
Lisa Singh
Keyword(s):  
Social Media ◽  
Topic Modeling ◽  
Topic Models ◽  
Next Generation ◽  
Text Corpus ◽  
Depth Analysis ◽  
Different Types ◽  
New Research ◽  
Evaluation Approaches ◽  
Contemporary Topic

Topic models have been applied to everything from books to newspapers to social media posts in an effort to identify the most prevalent themes of a text corpus. We provide an in-depth analysis of unsupervised topic models from their inception to today. We trace the origins of different types of contemporary topic models, beginning in the 1990s, and we compare their proposed algorithms, as well as their different evaluation approaches. Throughout, we also describe settings in which topic models have worked well and areas where new research is needed, setting the stage for the next generation of topic models.

scholarly journals Text-mining open-ended survey responses using structural topic modeling: A practical demonstration to understand parents’ coping methods during COVID-19 pandemic in Singapore

2021 ◽  
Author(s):  
Gerard Chung ◽  
Maria Rodriguez ◽  
Paul Lanier ◽  
Daniel Gibbs
Keyword(s):  
Text Mining ◽  
Topic Modeling ◽  
Topic Model ◽  
Research Process ◽  
Estimation Model ◽  
Coping Methods ◽  
Model Interpretation ◽  
Textual Data ◽  
Survey Responses ◽  
Structural Topic Modeling

Objective: Open-ended survey questions crucially contribute to researchers’ understandings of respondents’ experiences. However, analyzing open-ended responses using human coders is labor-intensive and prone to inconsistencies. Structural topic modeling (STM) is a text mining method that discover topics from textual data. We demonstrate the use of STM to analyze open- ended survey responses to understand how parents cope during COVID-19 lock-down in Singapore. Method: We administered online surveys to 199 parents in Singapore during the COVID-19 lock-down. To show a STM analysis, we demonstrated a workflow that includes steps in data preprocessing, model estimation, model selection, and model interpretation. Results: An 18-topic model best fitted the data based on model diagnostics and researchers’ expertise. Prevalent coping methods described by respondents include “Spousal Support”, “Routines/Schedules” and “Managing Expectations”. Topic prevalence for some topics varies with respondents’ levels of parenting stress and whether parents were fathers or mothers. Conclusion: STM offers an efficient, valid, and replicable way to analyze textual data such as open-ended survey responses and case notes that can complement researchers’ knowledge and skills. STM can be used as part of a multistage research process or to support other analyses such as clarifying quantitative findings and identifying preliminary themes from qualitative data.

Investigating Diseases and Chemicals in COVID-19 Literature with Text Mining (Preprint)

2020 ◽  
Author(s):  
Amir Karami ◽  
Brandon Bookstaver ◽  
Melissa Nolan
Keyword(s):  
Text Mining ◽  
Literature Review ◽  
Topic Modeling ◽  
Large Scale ◽  
Clinical Manifestations ◽  
International Health ◽  
Research Papers ◽  
Strategic Plans ◽  
Funding Agencies ◽  
The Relationship

BACKGROUND The COVID-19 pandemic has impacted nearly all aspects of life and has posed significant threats to international health and the economy. Given the rapidly unfolding nature of the current pandemic, there is an urgent need to streamline literature synthesis of the growing scientific research to elucidate targeted solutions. While traditional systematic literature review studies provide valuable insights, these studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, incapable of trend analysis, and lack of data-driven tools. OBJECTIVE This study fills the mentioned restrictions in the literature and practice by analyzing two biomedical concepts, clinical manifestations of disease and therapeutic chemical compounds, with text mining methods in a corpus containing COVID-19 research papers and find associations between the two biomedical concepts. METHODS This research has collected papers representing COVID-19 pre-prints and peer-reviewed research published in 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling to find the relationship between the two biomedical concepts. RESULTS We analyzed 9,298 research papers published through May 5, 2020 and found 3,645 disease-related and 2,434 chemical-related articles. The most frequent clinical manifestations of disease terminology included COVID-19, SARS, cancer, pneumonia, fever, and cough. The most frequent chemical-related terminology included Lopinavir, Ritonavir, Oxygen, Chloroquine, Remdesivir, and water. Topic modeling provided 25 categories showing relationships between our two overarching categories. These categories represent statistically significant associations between multiple aspects of each category, some connections of which were novel and not previously identified by the scientific community. CONCLUSIONS Appreciation of this context is vital due to the lack of a systematic large-scale literature review survey and the importance of fast literature review during the current COVID-19 pandemic for developing treatments. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, to journals for exploring most discussed disease symptoms and pharmaceutical targets, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.

scholarly journals A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter

2021 ◽  
Author(s):  
Irina Wedel ◽  
Michael Palk ◽  
Stefan Voß
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Topic Modeling ◽  
New Product ◽  
Business Value ◽  
Data Driven ◽  
New Product Introduction ◽  
Social Media Analytics ◽  
Product Introduction ◽  
Textual Data

AbstractSocial media enable companies to assess consumers’ opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.

Preschoolers' Imaginativeness: Subtypes, Correlates, and Maladaptive Extremes

1985 ◽  
Vol 4 (4) ◽  
pp. 349-364 ◽  
Author(s):  
Roni Beth Tower
Keyword(s):  
Preschool Children ◽  
Child Development ◽  
Healthy Child ◽  
Observational Studies ◽  
Two Dimensions ◽  
Different Types ◽  
Research Questions ◽  
Negative Implication

In a study of forty-three preschool children, ratings of four types of the children's imaginativeness were correlated with observational, behavioral, and interview measures. Research questions were: 1) Do correlates of imaginativeness found in observational studies replicate if trait rather than state measures are examined? 2) Do different types of imaginativeness have different correlates? and 3) What characteristics distinguish children at the maladaptive extremes of imaginativeness from those at more moderate levels? The conceptual and empirical utility of considering imaginativeness to have two dimensions, Expressive and Constructive, was demonstrated. While optimal levels of Constructive Imaginativeness correlated significantly with other indices of healthy child development, the correlations were fewer and tended to be weaker for Expressive Imaginativeness. The negative implication of extremes was documented.

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