Benefits of Unstructured Data for Industrial Quality Analysis

Hänig, Christian; Schierle, Martin; Trabold, Daniel

doi:10.1007/978-1-4614-0373-9_20

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…It has been an important part of theory‐based research as theory is operationalized into a specific measurable form. In comparison, unstructured data require a different, more thorough approach to hold value (Hänig et al, 2010).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

What is work engagement? A text mining approach using employees' self‐narratives

van Roekel,

Wigger,

Veldkamp

et al. 2023

Applied Psychology

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We introduce text mining to study work engagement by using this method to classify employees' survey‐based self‐narratives into high or low work engagement and analyzing the text features that contribute to the classification. We used two samples, representing the 2020 and 2021 waves of an annual survey among healthcare employees. In the first study, we used exploratory sample 1 (N = 5591) to explore which text features explain work engagement (unigrams, bigrams, psychological, or linguistic). In the second study, we confirmed whether features persisted over time between exploratory sample 1 and confirmatory sample 2 (N = 4470). We find that psychological features classify employees across two samples with 60% accuracy. These features partly validate the literature: High‐engaged employees refer more to affiliation and positive emotions, and low‐engaged employees refer more to negative emotions and power. We extend the literature by studying linguistics: High‐engaged employees use more first‐person plural (“we”) than low‐engaged employees. Finally, some results question the literature, like the finding that low‐engaged employees refer more to their managers. This study shows text mining can contribute by confirming, extending, or questioning the literature on work engagement and explores how future research could build on our findings with survey‐based or in vivo applications.

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Section: Theoretical Backgroundmentioning

confidence: 99%

What is work engagement? A text mining approach using employees' self‐narratives

van Roekel,

Wigger,

Veldkamp

et al. 2023

Applied Psychology

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“…These are some of the main reasons for NLP not to be used as a single approach for analyzing unstructured textual data. Consequently, other tools and processes are in need to be developed as add-on to NLP and to extract context for business intelligence and deliver value to companies (Hänig & Schierle, 2010). Also, owning to the dramatic changes that occurred with the more diverse communication channels and sources, the evolution of contextualization reached to text mining.…”

Section: Conceptualization Of Unstructured Datamentioning

confidence: 99%

Business demands for processing unstructured textual data – text mining techniques for companies to implement

Zhecheva¹,

Nenkov²

2022

ACCESS

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The rapid development of technology has caused a pervasive change in the way people and businesses live. Making sound business decisions is unthinkable without processing a large amount of data (publicly available and collected on the basis of problems) with high accuracy and quality. The importance of unstructured data acquires various sources is growing. Of particular value is the continuous flow of textual information that is generated every minute around the world in a different form (unstructured textual data). This is also the subject of this article. The aim of the article is to provide an analytical overview of the main methods of word processing that are applicable for pragmatic analysis of information flows from companies, such as: extraction, summarization, grouping and categorization of text. Some methodologies are based on NLP (Natural Language Processing), others on Bayesian logic and statistical theory and practice. From the review of various publications on the topic, conclusions are proposed for their practical applicability. This allows for an objective choice of appropriate tools for processing unstructured information and business intelligence. The results of the study can be successfully used to improve managerial decision-making, improve the quality of work of employees and reduce errors in overall marketing planning.

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“…These data can be either structured or unstructured, and the amount of unstructured data is increasing more rapidly than the amount of structured data [2,3]. In spite of these facts, data analysts still mainly depend on structured data when analyzing data in practice [4,5]. This indicates that there is considerable room for improvement with regard to harnessing unstructured data.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Transforming Unstructured Data into Structured Data on a Churn Prediction Model for Loan Customers

2020

KSII TIIS

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Benefits of Unstructured Data for Industrial Quality Analysis

Cited by 4 publications

References 3 publications

What is work engagement? A text mining approach using employees' self‐narratives

What is work engagement? A text mining approach using employees' self‐narratives

Business demands for processing unstructured textual data – text mining techniques for companies to implement

The Impact of Transforming Unstructured Data into Structured Data on a Churn Prediction Model for Loan Customers

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