Opening up to big data: computer-assisted analysis of textual data in social sciences

"Two developments in computational text analysis may change the way qualitative data analysis in social sciences is performed: 1. the availability of digital text worth to investigate is growing rapidly, and 2. the improvement of algorithmic information extraction approaches, also called text mining, allows for further bridging the gap between qualitative and quantitative text analysis. The key factor hereby is the inclusion of context into computational linguistic models which extends conventional computational content analysis towards the extraction of meaning. To clarify methodological differences of various computer-assisted text analysis approaches the article suggests a typology from the perspective of a qualitative researcher. This typology shows compatibilities between manual qualitative data analysis methods and computational, rather quantitative approaches for large scale mixed method text analysis designs." (author's abstract

Chapter 21 From Frequency Counts to Contextualized Word Embeddings

Text, the written representation of human thought and communication in natural language, has been a major source of data for social science research since its early beginnings. While quantitative approaches seek to make certain contents measurable, for example through word counts or reliable categorization (coding) of longer text sequences, qualitative social researchers put more emphasis on systematic ways to generate a deep understanding of social phenomena from text. For the latter, several qualitative research methods such as qualitative content analysis (Mayring, 2010), grounded theory methodology (Glaser & Strauss, 2005), and (critical) discourse analysis (Foucault, 1982) have been developed. Although their methodological foundations differ widely, both currents of empirical research need to rely to some extent on the interpretation of text data against the background of its context. At the latest with the global expansion of the internet in the digital era and the emergence of social networks, the huge mass of text data poses a significant problem to empirical research relying on human interpretation. For their studies, social scientists have access to newspaper texts representing public media discourse, web documents from companies, parties, or NGO websites, political documents from legislative processes such as parliamentary protocols, bills and corresponding press releases, and for some years now micro-posts and user comments from social media. Computational support is inevitable even to process samples of such document volumes that could easily comprise millions of documents

Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings

Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018), Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in NLP. CWEs provide semantic vector representations of words depending on their respective context. Their advantage over static word embeddings has been shown for a number of tasks, such as text classification, sequence tagging, or machine translation. Since vectors of the same word type can vary depending on the respective context, they implicitly provide a model for word sense disambiguation (WSD). We introduce a simple but effective approach to WSD using a nearest neighbor classification on CWEs. We compare the performance of different CWE models for the task and can report improvements above the current state of the art for two standard WSD benchmark datasets. We further show that the pre-trained BERT model is able to place polysemic words into distinct 'sense' regions of the embedding space, while ELMo and Flair NLP do not seem to possess this ability.Comment: 10 pages, 3 figures, 6 tables, Accepted for Konferenz zur Verarbeitung nat\"urlicher Sprache / Conference on Natural Language Processing (KONVENS) 2019, Erlangen/German

What is the REFI-QDA Standard: Experimenting With the Transfer of Analyzed Research Projects Between QDA Software

The open REFI-QDA standard allows for the exchange of entire projects from one QDA software to another, on condition that software vendors have built the standard into their software. To reveal the new opportunities emerging from overcoming QDA software lock-in, we describe an experiment with the standard in four separate research projects done by several researchers during a week at the Lorentz Centre (The Netherlands) in August 2019. Each researcher exchanged some processed research data between two qualitative data analysis software (QDAS) packages. We start by envisaging the development of the REFI-QDA standard, followed by the context of each research project, the type(s) of data in it, the reasons for wanting to do the transfer to another program and the lessons learnt in doing so. We conclude with the benefits of the REFI-QDA standard and the issues to be taken into account when considering a transfer between QDAS.Der offene REFI-QDA-Standard ermöglicht es, Daten zwischen verschiedenen QDA-Programmen auszutauschen, vorausgesetzt, die Softwarehersteller*innen haben den Standard in ihre Programme integriert. Um die neuen Möglichkeiten aufzuzeigen, die sich aus dieser Überwindung der Beschränkung auf einzelne Analyseprogramme ergeben, beschreiben wir die Ergebnisse eines Experiments zur Nutzung des Standards in vier verschiedenen Forschungsprojekten. Das Experiment wurde im August 2019 am Lorentz Centre (Niederlande) durchgeführt. Alle beteiligten Forscher*nnen tauschten mithilfe des Standards zuvor verarbeitete Forschungsdaten zwischen zwei Softwarepaketen zur qualitativen Datenanalyse (QDAS) aus. Unser Artikel beginnt zunächst damit, die Entstehungsgeschichte des REFI-QDA Standards zu erläutern. Anschließend stellen wir die einzelnen Forschungsprojekte samt der darin verwendeten Daten vor und erläutern die Beweggründe für deren Übertragung in eine zweite QDA-Software sowie die daraus gewonnenen Erkenntnisse. Im letzten Schritt fassen wir den Mehrwehrt des REFI-QDA-Standards zusammen und diskutieren Aspekte, die bei einem Transfer zwischen QDAS zu beachten sind