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ABSTRACTIn the last decade, text-analytic methods have become a fundamental element of a political researcher's toolkit. Today, text analysis is taught in most major universities; many have entire courses dedicated to the topic. This article offers a systematic review of 45 syllabi of text-analysis courses around the world. From these syllabi, we extracted data that allowed us to rank canonical sources and discuss the variety of software used in teaching. Furthermore, we argue that our empirical method for building a text-analysis syllabus could easily be extended to syllabi for other courses. For instance, scholars can use our technique to introduce their graduate students to the field of systematic reviews while improving the quality of their syllabi.

Content: Erhard Mergenthaler: Computer-assisted content analysis (3-32); Udo Kelle: Computer-aided qualitative data analysis: an overview (33-63); Christian Mair: Machine-readable text corpora and the linguistic description of danguages (64-75); Jürgen Krause: Principles of content analysis for information retrieval systems (76-99); Conference Abstracts (100-131).

Les auxiliaires modaux dans l'analyse textuelle. Les verbes auxiliaires modaux tels que devoir, vouloir... fournissent des informations concernant les intentions des sujets sémantiques dans les morceaux de phrase où ils apparaissent. Par exemple, en déclarant qu'une personne doit agir d'une certaine manière, on montre la différence entre l'action et la possibilité que la personne agisse autrement. Cette information peut être utilisée dans la recherche. L'analyse textuelle sémantique permet le codage des auxiliaires modaux. Cet article examine comment faire pour garder en présence les auxiliaires modaux lors une analyse textuelle par réseaux. Ce type d'analyse textuelle nous permet de traiter des argumentations assez complexes, mais le résultat montre que le chercheur ne doit pas utiliser l'analyse par réseaux dans ces cas et plutôt employer l'analyse sémantique.

UID/CPO/04627/2013, SFRH/BPD/78955/2011. ; This paper reviews the logic of attempts to automate the processes involved in computer-assisted text analysis in the social sciences. Bayesian estimation methods in spatial analysis of variations in positions of political parties over time and Latent Dirichlet Allocation from the developing field of latent topic analysis are compared with the analysis of structures of word co-occurrences in the tradition of content analysis, using Procrustean individual differences scaling. Each depends in practice on concentrating attention on a limited number of word tokens regarded as meaningful while most are disregarded as inessential. By applying apparently competing strategies to the same set of party contributions to the 1997 budget debate in the Italian parliament, they can beshown to be complementary in character and should be applied as such in comparing material of this kind. ; publishersversion ; published

This paper reviews the logic of attempts to automate the processes involved in computer-assisted text analysis in the social sciences. Bayesian estimation methods in spatial analysis of variations in positions of political parties over time and Latent Dirichlet Allocation from the developing field of latent topic analysis are compared with the analysis of structures of word co-occurrences in the tradition of content analysis, using Procrustean individual differences scaling. Each depends in practice on concentrating attention on a limited number of word tokens regarded as meaningful while most are disregarded as inessential. By applying apparently competing strategies to the same set of party contributions to the 1997 budget debate in the Italian parliament, they can be shown to be complementary in character and should be applied as such in comparing material of this kind.

In political science, research using computer-assisted text analysis techniques has exploded in the last fifteen years. This scholarship spans work studying political ideology,1 congres-sional speech,2 representational style,3 American foreign policy,4 climate change attitudes,5 media,6 Islamic clerics,7 and treaty making,8 to name but a few. As these examples illustrate, com-puter-assisted text analysis—a prime example of mixed-meth-ods research—allows gaining new insights from long-familiar political texts, like parliamentary debates, and altogether en-ables the analysis to new forms of political communication, such as those happening on social media.

I provide a visual representation of keyword trends and authorship for two flagship sociology journals using data from JSTOR's Data for Research repository. While text data have accompanied the digital spread of information, it remains inaccessible to researchers unfamiliar with the required preprocessing. The visualization and accompanying code encourage widespread use of this source of data in the social sciences.

AbstractMuch of social life now takes place online, and records of online social interactions are available for social science research in the form of massive digital text archives. But cultural social science has contributed little to the development of machine‐assisted text analysis methods. As a result few text analysis methods have been developed that link digital text data to theories about culture and discourse. This paper attempts to lay the groundwork for development of such methods by proposing metatheoretical and theoretical foundations suitable for machine‐assisted semantic text analysis. Metatheoretically I draw on the work ofElder‐Vass (2012),Kaidesoja (2013) and others to argue that digital text analysis methods ought to be (and in practice implicitly are) based on a realist constructionist ontology that treats discourses as ontologically real emergent social entities that have causal relationships with non‐discursive social and cognitive processes. Theoretically I followFeldman (2006) and many others in arguing that language is fundamentally shaped by processes of embodied cognition. Researchers developing digital text analysis techniques must theoretically account for such processes if they wish to produce algorithms that can interpret texts in ways that supplement, and not only amplify, human interpretation. I critically survey contemporary text analysis methods that implicitly share these metatheoretical and theoretical positions and discuss some ways these can be further developed with newly available software.

AbstractThe amount of digital text available for analysis by consumer researchers has risen dramatically. Consumer discussions on the internet, product reviews, and digital archives of news articles and press releases are just a few potential sources for insights about consumer attitudes, interaction, and culture. Drawing from linguistic theory and methods, this article presents an overview of automated text analysis, providing integration of linguistic theory with constructs commonly used in consumer research, guidance for choosing amongst methods, and advice for resolving sampling and statistical issues unique to text analysis. We argue that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided. Text analysis can be used to examine psychological and sociological constructs in consumer-produced digital text by enabling discovery or by providing ecological validity.

This article considers experience of applying such sociological methods as content analysis and information-goal analysis to studying the heritage of representatives of pre-revolution Russian social conception (P. I. Novgorod- tsev, B. P. Vysheslavtsev, I. A. Ilyin and N. N. Alekseev). The author applies methodological model of semantic and structural analysis based on combi­nation of classical qualitative and quantitative content analysis and infor­mation-goal analysis.

The article deals with the poetics of the comic, the main focus is on the categories of analysis of individual comic segments. The material for the study was the prose of Nikolai Gogol. According to scientific literature, a comic work can be described not only as an aesthetic phenomenon but also as a cognitive construction and a special symbolic practice, which makes it possible to differentiate comic elements according to various criteria. A close reading of a comic fragment shows that a relatively small amount of text can contain many comic components, between which structural, content, and pragmatic differences can be detected. The comic text is considered through the prism of a set of dichotomies: active laughter – reduced laughter, satire – humor, comic deviation – nonsense, a simple comic element – a complex comic element. As shows an analysis of examples of reduced laughter (the term coined by M. M. Bakhtin), reduction of the comic is often associated with the loss (or incomplete realization) in the segment of special interdiscursivity. Grasping the dichotomy of satire and humor requires taking into account the diversity of intentions realized in satire: it can not only ridicule anomalous objects but also destroy the norm itself. Among the anomalies underlying the comic game, it is possible to distinguish deviations (shifts from the norm) and nonsense. A comic text based on nonsense requires special interpretive scenarios (scripts), in particular, it is proposed to distinguish between nonsense as such and absurdity. Distinguishing comic routines that are complex (in terms of their structure of expression) significantly complicates the issue of quantitative analysis of the comic: the volume of comic segments and the volume of comic effects are not fundamentally equivalent.