Taming Textual Data: The Contribution of Corpus Linguistics to Computer-Aided Text Analysis

Pollach, Irene

doi:10.1177/1094428111417451

Cited by 132 publications

(125 citation statements)

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“…These same characteristics also make it difficult to employ traditional quantitative (multivariate) research methods for online data, as it cannot be easily coded. Recent methodological advances have addressed these obstacles by developing new types of methods that use computation to process large sets of "big" textual data, blurring the traditional boundaries between qualitative and quantitative methods (Klüver, 2015;Light, 2014;Marciniak, 2016;Pollach, 2012). Such methods may include, for example, text mining (e.g.…”

Section: Analysing Online Datamentioning

confidence: 99%

Using online data and network-based text analysis in HRM research

Platanou

Mäkelä

Beletskiy

et al. 2017

JOEPP

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Purpose The purpose of this paper is to propose new directions for human resource management (HRM) research by drawing attention to online data as a complementary data source to traditional quantitative and qualitative data, and introducing network text analysis as a method for large quantities of textual material. Design/methodology/approach The paper first presents the added value and potential challenges of utilising online data in HRM research, and then proposes a four-step process for analysing online data with network text analysis. Findings Online data represent a naturally occuring source of real-time behavioural data that do not suffer from researcher intervention or hindsight bias. The authors argue that as such, this type of data provides a promising yet currently largely untapped empirical context for HRM research that is particularly suited for examining discourses and behavioural and social patterns over time. Practical implications While online data hold promise for many novel research questions, it is less appropriate for research questions that seek to establish causality between variables. When using online data, particular attention must be paid to ethical considerations, as well as the validity and representativeness of the sample. Originality/value The authors introduce online data and network text analysis as a new avenue for HRM research, with potential to address novel research questions at micro-, meso- and macro-levels of analysis.

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Section: Analysing Online Datamentioning

confidence: 99%

Using online data and network-based text analysis in HRM research

Platanou

Mäkelä

Beletskiy

et al. 2017

JOEPP

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“…Examples of this inductive construction of categories are approaches such as automatic speech analysis, or lexicometry, and the so-called corpus linguistics, in which the computational phase of the work precedes the interpretation [Pollach, 2012;Cheng et al, 2008]. More generally, text mining is precisely the attempt to make a semantic analysis of the texts and their structure [Wiedemann, 2013], extracting the "sense" by means of statistical methods that identify intrinsic characteristics of the corpus along with more interpretative aspects introduced by human coders.…”

Section: Automatic Coding and Computer-built Categoriesmentioning

confidence: 99%

“…Finally, in the analysis of texts as such, the software helps in at least three different ways, depending on the deductive or inductive approach and on the type of operation of text encoding -automatic or manual (for a taxonomy of the types of computer-aided analyses see, for example, Wiedemann [2013] and Pollach [2012]). …”

Section: Typologies and Processes Of Computer-assisted Text Analysismentioning

confidence: 99%

Computer-aided text analysis: an open-aired laboratory for social sciences

Castelfranchi¹

2017

JCOM

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Thanks, on the one hand, to the extraordinary availability of colossal textual archives and, on the other hand, to advances in computational possibilities, today the social scientist has at their disposal an extraordinary laboratory, made of millions of interacting subjects and billions of texts. An unprecedented, yet challenging, opportunity for science. How to test, corroborate models? How to control, interpret and validate Big Data? What is the role of theory in the universe of patterns and statistical correlations? In this article, we will show some general characteristics of the use of computational tools for the analysis of texts, and some applications in the areas of public communication of S&T and Science and Technology Studies (STS), also showing some of their limitations and pitfalls. AbstractPublic understanding of science and technology; Representations of science and technology; Science communication: theory and models KeywordsThe information deluge has flooded social sciences. Governments around the world digitalize and make available laws and projects, parliamentary debates and questions, social and economic indicators, party funding, government programs, polls and public opinion polls, maps, and models, due to new governance practices (and the new rhetoric) of transparency, accountability, participation and deliberation. Dynamics of globalized markets and customer demand force large and small media outlets (newspapers and magazines, TVs and radios, network on the Internet and on social networks) to make it possible to download huge databases, including archives of what was written, said, sung over a period of decades.Publishers publish their books in digital format. Libraries and newspaper libraries, as well as corporations such as Google, file millions of publications containing a considerable amount of literature and world history. New forms of sociability, on and offline, new modes, effects and affections of contemporary individualism lead tens of millions of users to illustrate, narrate, edit, and caption on web 2.0 their tastes and intimacies, their daily life and their memories, the interactions, associations, conflicts, dreams, resentments and confessions. We are exposed to millions of gigabytes of information and, at the same time, we are tireless

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“…Depending on the epistemological position taken, such social media speak are either (1) accounts of what customers do or (2) symbolical reflections on customers' intentions (Ludwig et al 2013, Ludwig et al 2014, Pollach 2012, Taylor and van Every 2010. For both positions, the sheer volume of online conversations and their unstructured, verbatim nature renders traditional market research methods (e.g., surveys, experiments, interviews, focus groups) ineffective (Kambil et al, 2005).…”

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confidence: 99%

“…Therefore, the advantages of using software for the analysis of textual data are obvious, and given the ample availability of verbatim data in social media, the question is no longer whether or not to use computer-aided text analysis, but how to approach a given dataset (cf. Pollach 2012). Methodologically, the central premise of text mining is based on the assumption that the frequency with which particular words and concepts occur in a text is a measure of their relative importance, attention or emphasis (Krippendorff, 2004).…”

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confidence: 99%

Decoding social media speak: developing a speech act theory research agenda

Ludwig

Ruyter

2016

Journal of Consumer Marketing

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Purpose-Drawing on the theoretical domain of speech act theory and a discussion of its suitability for setting the agenda for social media research, this study explores a range of research directions that are both relevant and conceptually robust, to stimulate the advancement of knowledge and understanding of online verbatim data. Permanent repository link:Design/methodology/approach-Examining previously published cross-disciplinary research, the study identifies how recent conceptual and empirical advances in speech act theory may further guide the development of text analytics in a social media context.Findings-Decoding content and function word use in customers' social media communication can enhance the efficiency of determining (1) potential impacts of customer reviews, (2) sentiment strength, (3) the quality of contributions in social media, (4) customers' socialization perceptions in online communities, and (5) deceptive messages.Originality/value-Considering the variety of managerial demand, increasing and diverging social media formats, expanding archives, rapid development of software tools, and fastpaced market changes, this study provides an urgently needed, theory-driven, coherent 2 research agenda to guide the conceptual development of text analytics in a social media context. Keywords-Social media communication, Text mining, Speech act theoryPaper type-Conceptual paper "Language is the currency of most human social processes." (Chung and Pennebaker, 2007 p.343) The past decade has witnessed an incredible increase in the volume and importance of online social media conversations. Spanning virtually all ages, ethnicities, and geographies, the spectacular rise of websites such as Facebook, LinkedIn, and Twitter is revolutionizing the way people communicate and interact with friends, family, colleagues, complete strangers, and businesses. The increasing prevalence and accessibility of social media communication and online discussions, together with their powerful influence on purchase decisions and product/service evaluations, have become central driving forces for marketing decision makers (Bharadwaj et al., 2013). Sixty percent of Facebook users comment directly on the products and service they purchase (Incite, 2011), and 72% of consumers expect brands to respond within an hour to complaints posted on Twitter (Gesenhus 2013). Business performance in turn are suffering, as a result of companies' inability to decipher and analyse conversations with and among their online customers (Bonnet and Nandan, 2011).Considering the scale and scope of company-relevant exchanges, it is pertinent that marketers monitor, join, and come to grips with efforts to decode "social media speak." Therefore the Following a brief introduction to this linguistic theory and its suitability for setting the research agenda, we explore a range of research directions that are both relevant and conceptually rob...

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Taming Textual Data: The Contribution of Corpus Linguistics to Computer-Aided Text Analysis

Cited by 132 publications

References 106 publications

Using online data and network-based text analysis in HRM research

Using online data and network-based text analysis in HRM research

Computer-aided text analysis: an open-aired laboratory for social sciences

Decoding social media speak: developing a speech act theory research agenda

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