Content Analysis of Hospital Reviews From Differing Sources: Does Review Source Matter?

Social media has had an impact on how patients find and evaluate medical professionals and their experiences of modern healthcare. Qualitative research in healthcare has increased its focus on social media. The present study examined 497 reviews of hospitals in the Pittsburgh area across three websites: Google, Yelp, and Healthgrades. Using computerized content analysis tools (CATA), we analyzed positive and negative comments to identify key themes. Key themes and words included “doctor,” “hospital,” “staff,” and “time.” These findings highlight the importance of medical staff to patient experience. Results indicated that Yelp had the lowest average rating. CATA also revealed that the central term for Google reviews was “hospital,” for Healthgrades reviews it was “doctor,” and the central term for Yelp reviews was “patient.” These central terms reflect the focus of each website. The present study highlights the importance of healthcare professionals understanding the source of reviews and being cautious about how social media comments are used in decision-making about the practice. Future research should try to expand this approach to other cities and countries to evaluate cross-cultural effects on social media comments.

Innovations in Text Analysis: Review of the International Conference MAXDAYS2

The article analyzes the conference held online by the German company VERBI Software GmbH - a developer of the well-known MAXQDA software designed for computerized content analysis of texts. Over the past decade Russian undergraduate and graduate students have been actively using this program in their research work as evidenced by a number of publications including in the "Istoricheskaya Informatika" journal. The author focuses on the dynamics of topics of the annual 2019-2021 conferences and characterizes the latest online conference. The author draws attention to the fact that the online status of the conference resulted in a sharp increase in the number of participants in comparison with previous traditional conferences. The novelty of this study is the fact that the author compares the reports of three conferences focusing on the range of methodological and methodical issues of computerized text analysis, constant update of research tools, new trends in the provision of educational materials to program users including open access to Maxqda Press publications. The article provides useful links to conference materials posted both on the developer's website and on YouTube. The author pays attention to those tools (platforms for online conferences) which are used by software developers to attract the maximum number of participants: free conference registration, user-friendly interface and international aware work (9 languages).

Historiometric Methods

Historiometrics is a quantitative and nomothetic method for the study of aesthetic masterpieces, artistic geniuses, and the sociocultural context in which those masterpieces emerge and their creators operate. The technique specifically depends on the scientific analysis of biographical and historical data, including the (often computerized) content analysis of actual works. The units of analysis can range from products to creators to whole eras or periods. Historiometric research can sometimes rely on standard statistical methods, such as factor analysis and multiple regression, but often more advanced techniques are required, such as hierarchical linear modeling. Although historiometric research on the arts has been carried out since 1835, the practice remains less common than other methods used in empirical aesthetics. Even so, it provides the investigator with the unique opportunity to examine the psychological and sociocultural foundations of genuine masterworks in literature, music, the visual arts, and cinema.

Computerized content analysis of online data – opportunities for marketing scholars and practitioners

Purpose The purpose of this paper is to summarize the main developments from the early days of manual content analysis to the adoption of computer-assisted content analysis and the emerging artificial intelligence (AI)-supported ways to analyze content (primarily text) in marketing and consumer research. A further aim is to outline the many opportunities these new methods offer to marketing scholars and practitioners facing new types of data. Design/methodology/approach This conceptual paper maps our methods used for content analysis in marketing and consumer research. Findings This paper concludes that many new and emerging forms of unstructured data provide a wealth of insight that is neglected by existing content analysis methods. The main findings of this paper support the fact that emerging methods of making sense of such consumer data will take us beyond text and eventually lead to the adoption of AI-supported tools for all types of content and media. Originality/value This paper provides a broad summary of nearly five decades of content analysis in consumer and marketing research. It concludes that, much like in the past, today’s research focuses on the producers of the words than the words themselves and urges researchers to use AI and machine learning to extract meaning and value from the oceans of text and other content generated by organizations and their customers.

The relationship between pop music and lyrics: A computerized content analysis of the United Kingdom’s weekly top five singles, 1999–2013

The majority of research on music aesthetics treats music and lyrics as discrete entities, despite the artistic imperative that they should relate to one another in some way. This research computer analyzed both the music and lyrics of the songs to have reached the weekly UK top five singles chart from January 1999 to December 2013 ( N = 1,414). The findings indicate that the typicality of a given set of lyrics relative to the corpus as a whole was associated with their popularity; that there were numerous associations between each of six mood scores assigned to the music and various aspects of the lyrics (e.g., passionate music was associated with lyrics addressing hardship and less concern with precise numerical terms); and that the relative contribution of the lyrics and music to overall popularity varied according to the means by which these were operationalized so that, for instance, music and lyrics contributed equally to explaining peak chart position, whereas music outperformed lyrics in explaining the number of weeks spent on the top five. Pop music and its lyrics are related to one another, and the relationship can be explained to some extent via existing concepts in the aesthetics literature.

Teaching Computerized Content Analysis for Undergraduate Research Papers

ABSTRACTContent analysis is the process of turning text into data, with either automated or manual techniques, and it provides a feasible and attractive option for undergraduate students to develop and utilize original data. This article presents a cohesive framework for teaching computerized content analysis in undergraduate political science courses. The article discusses examples of how we have taught the techniques in our own classrooms and provides a framework for a content-analysis research assignment. We describe coding, sources of text data available to students, software recommendations appropriate for students, and write-up issues. In the process, we also discuss various learning opportunities that arise from both the strengths and weaknesses of computerized content analysis as a methodological strategy.

Narrative Sequence Position of Close-Ups Influences Cognitive and Affective Processing and Facilitates Theory of Mind

Close-up shots have been shown to modulate affective, cognitive and theory-of-mind responding to visual narratives. However, the role of close-up’s narrative-sequence position, that is the relative timing of close-up shots in a visual narrative, is largely unknown. Participants watched one of ten versions of the same animated film, after we inserted a close-up shot (neutral or a sad face) at one of five different time points. Story recall responses of 168 participants were analyzed by the Linguistic Inquiry of Word Count, a computerized content analysis software, and coded manually for theory of mind. The narrative-sequence position of the close-up influenced the level of cognitive processing, affective processing, and theory of mind evident in participant responses where a U-shaped relationship was observed for the close-up position. These findings further our understanding of how close-ups affect narrative processing and are of relevance for studies on formal features in visual narratives.