Text Mining: A Guidebook for the Social Sciences

Ignatow, Gabe; Mihalcea, Rada

doi:10.4135/9781483399782

Cited by 92 publications

(67 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Such an analysis investigates the function that the narrative (mode of cognition) serves, in helping people make sense of their lives, particularly by shaping chaotic, unexpected, or anomalous events into a coherent story. The narrative (mode of cognition) might solve various functions, such as dealing with problems, reducing tensions, or resolving dilemmas (Bruner, 1990; see also Ignatow and Mihalcea, 2016). There are a number of ways in which this may occur.…”

Section: Analytical Approachmentioning

confidence: 99%

Integrated reporting and narrative accountability: the role of preparers

Lai

Melloni

Stacchezzini

2018

AAAJ

112

123

View full text Add to dashboard Cite

Purpose-The International Integrated Reporting Council (IIRC) claims that integrated reporting (IR) can enhance corporate accountability, yet critical and interpretative studies have contested this outcome. Insufficient empirical research details how preparers experience accountability while constructing IR; to fill this gap, this study analyses how the preparers' mode of cognition influences the patterns of accountability associated with IR. Design/methodology/approach-A functionalist approach to narratives helps elucidate the role that the IR preparers' narrative mode of cognition plays on accountability toward stakeholders. The empirical analysis particularly benefits from in-depth interviews with the IR preparers of a global insurer that has used IR since 2013. Findings-The preparers' narrative mode of cognition facilitates dialogue with IR users. It addresses accountability tensions by revealing the company's value creation process. Preparers' efforts to establish a meaningful dialogue with a growing variety of stakeholders through broader and plainer messages reveals the potential of IR as a narrative source of a socializing form of accountability. However, financial stakeholders remain the primary addressees of the reports. Research limitations/implications-This study focuses on preparers' views; further research should integrate users' accountability expectations. Originality/value-This article offers new insights for dealing with corporate reporting and accountability in a novel IR setting.

show abstract

Section: Analytical Approachmentioning

confidence: 99%

Integrated reporting and narrative accountability: the role of preparers

Lai

Melloni

Stacchezzini

2018

AAAJ

112

123

View full text Add to dashboard Cite

show abstract

“…Natural language processing is a field of linguistic science that seeks to understand how computers can comprehend and mimic human language (Landers, 2017;Manning & Schütze, 1999); this includes a series of methods to classify, mine, and predict information from text data, otherwise known as text mining or text analytics. Text mining analyses range from fully supervised (i.e., keyed upon some criterion, such as expert ratings) to fully unsupervised approaches (Pang & Lee, 2008) and are used for a range of analytical tasks, including topical categorization, thematic analysis, sentiment analysis, author attribution, and personality measurement, among many others (e.g., Grimmer & Stewart, 2013;Ignatow & Mihalcea, 2017;Kobayashi et al, 2017aKobayashi et al, , 2017bLiu, 2012;Mairesse, Walker, Mehl, & Moore, 2007;Medhat, Hassan, & Korashy, 2014;Schwartz et al, 2013;Stamatatos, 2009). Text mining has increasingly been applied to the social sciences (e.g., Ignatow & Mihalcea, 2017;McAbee, Landis, & Burke, 2017), although within this domain, the practice is still in its infancy.…”

Section: Text Mining In a Job Performance Contextmentioning

confidence: 99%

Quantifying with words: An investigation of the validity of narrative‐derived performance scores

Speer

2018

Personnel Psychology

View full text Add to dashboard Cite

Performance appraisal research has focused almost entirely on traditional numerical ratings despite narrative text comments regularly being collected within appraisals. This study investigated the theory and utility of leveraging narrative comments to better understand employee performance. Narrative sentiment scores were derived using text mining on a large sample of narrative comments. These scores were then applied to an independent set of 2 years of performance data. It was assumed that narrative comments would reflect true performance variance that overlaps with traditional ratings, but also that they would capture incremental variance due to increases in total information and a reduction in rater‐motivated biases in contexts in which narrative data were not explicitly linked to administrative outcomes. The derived narrative scores were reliable across years, converged with traditional numerical ratings and explained incremental variance in future performance outcomes (performance ratings, involuntary turnover, promotions, and pay increases). Collectively, this study highlights how narratives can enhance performance measurement and demonstrates how these data can be economically scored in applied settings.

show abstract

“…Several analytic methods were used to strengthen the reliability of the findings, including hierarchical linear modeling (Raudenbush & Bryk, ) and propensity scores (Austin, ; Guo & Fraser, ). Moreover, text mining—a form of data mining that can “turn text into numbers”—facilitated the efficient processing of more than 67,000 open‐ended responses; with so much data, traditional qualitative methods were both impractical and inefficient (Ignatow & Mihalcea, ; Miner et al., , p. 30). Using the software program WordStat, I investigated themes across the entire collection of responses through univariate frequency analysis; feature extraction, via principal components analysis and keywords in context; cluster analysis; and sentiment analysis.…”

Section: Investigating Associations Between Work Leadership Capacitymentioning

confidence: 99%