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  1. Huang, X.; Soergel, D.; Klavans, J.L.: Modeling and analyzing the topicality of art images (2015) 0.05 
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    Abstract
    This study demonstrates an improved conceptual foundation to support well-structured analysis of image topicality. First we present a conceptual framework for analyzing image topicality, explicating the layers, the perspectives, and the topical relevance relationships involved in modeling the topicality of art images. We adapt a generic relevance typology to image analysis by extending it with definitions and relationships specific to the visual art domain and integrating it with schemes of image-text relationships that are important for image subject indexing. We then apply the adapted typology to analyze the topical relevance relationships between 11 art images and 768 image tags assigned by art historians and librarians. The original contribution of our work is the topical structure analysis of image tags that allows the viewer to more easily grasp the content, context, and meaning of an image and quickly tune into aspects of interest; it could also guide both the indexer and the searcher to specify image tags/descriptors in a more systematic and precise manner and thus improve the match between the two parties. An additional contribution is systematically examining and integrating the variety of image-text relationships from a relevance perspective. The paper concludes with implications for relational indexing and social tagging.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.8, S.1616-1644
  2. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment strength detection for the social web (2012) 0.05 
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    Abstract
    Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.163-173
  3. Bi, Y.: Sentiment classification in social media data by combining triplet belief functions (2022) 0.03 
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.7, S.968-991
  4. Chubin, D.E.; Moitra, S.D.: Content analysis of references : adjunct or alternative to citation counting? (1975) 0.03 
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    Source
    Social studies of science. 5(1975), S.423-441
  5. Sauperl, A.: Catalogers' common ground and shared knowledge (2004) 0.03 
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    Abstract
    The problem of multiple interpretations of meaning in the indexing process has been mostly avoided by information scientists. Among the few who have addressed this question are Clare Beghtol and Jens Erik Mai. Their findings and findings of other researchers in the area of information science, social psychology, and psycholinguistics indicate that the source of the problem might lie in the background and culture of each indexer or cataloger. Are the catalogers aware of the problem? A general model of the indexing process was developed from observations and interviews of 12 catalogers in three American academic libraries. The model is illustrated with a hypothetical cataloger's process. The study with catalogers revealed that catalogers are aware of the author's, the user's, and their own meaning, but do not try to accommodate them all. On the other hand, they make every effort to build common ground with catalog users by studying documents related to the document being cataloged, and by considering catalog records and subject headings related to the subject identified in the document being cataloged. They try to build common ground with other catalogers by using cataloging tools and by inferring unstated rules of cataloging from examples in the catalogs.
    Source
    Journal of the American Society for Information Science and technology. 55(2004) no.1, S.55-63
  6. White, M.D.; Marsh, E.E.: Content analysis : a flexible methodology (2006) 0.02 
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    Abstract
    Content analysis is a highly flexible research method that has been widely used in library and information science (LIS) studies with varying research goals and objectives. The research method is applied in qualitative, quantitative, and sometimes mixed modes of research frameworks and employs a wide range of analytical techniques to generate findings and put them into context. This article characterizes content analysis as a systematic, rigorous approach to analyzing documents obtained or generated in the course of research. It briefly describes the steps involved in content analysis, differentiates between quantitative and qualitative content analysis, and shows that content analysis serves the purposes of both quantitative research and qualitative research. The authors draw on selected LIS studies that have used content analysis to illustrate the concepts addressed in the article. The article also serves as a gateway to methodological books and articles that provide more detail about aspects of content analysis discussed only briefly in the article.
    Source
    Library trends. 55(2006) no.1, S.22-45
  7. Pejtersen, A.M.: Design of a computer-aided user-system dialogue based on an analysis of users' search behaviour (1984) 0.02 
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    Source
    Social science information studies. 4(1984), S.167-183
  8. Tibbo, H.R.: Abstracting across the disciplines : a content analysis of abstracts for the natural sciences, the social sciences, and the humanities with implications for abstracting standards and online information retrieval (1992) 0.02 
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    Abstract
    Reports on a comparison of the "content categories" listed in the ANSI/ISO abstracting standards to actual content found in abstracts from the sciences, social sciences, and the humanities. The preliminary findings question the fundamental concept underlying these standards, namely, that any one set of standards and generalized instructions can describe and elicit the optimal configuration for abstracts from all subject areas
  9. Bertola, F.; Patti, V.: Ontology-based affective models to organize artworks in the social semantic web (2016) 0.02 
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    Abstract
    In this paper, we focus on applying sentiment analysis to resources from online art collections, by exploiting, as information source, tags intended as textual traces that visitors leave to comment artworks on social platforms. We present a framework where methods and tools from a set of disciplines, ranging from Semantic and Social Web to Natural Language Processing, provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions. The ontology is inspired by the Plutchik's circumplex model, a well-founded psychological model of human emotions. Users can be involved in the creation of the emotional space, through a graphical interactive interface. The development of such semantic space enables new ways of accessing and exploring art collections. The affective categorization model and the emotion detection output are encoded into W3C ontology languages. This gives us the twofold advantage to enable tractable reasoning on detected emotions and related artworks, and to foster the interoperability and integration of tools developed in the Semantic Web and Linked Data community. The proposal has been evaluated against a real-word case study, a dataset of tagged multimedia artworks from the ArsMeteo Italian online collection, and validated through a user study.
    Footnote
    Beitrag in einem Themenheft "Emotion and sentiment in social and expressive media"
  10. Raieli, R.: ¬The semantic hole : enthusiasm and caution around multimedia information retrieval (2012) 0.02 
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    Date
    22. 1.2012 13:02:10
    Footnote
    Bezugnahme auf: Enser, P.G.B.: Visual image retrieval. In: Annual review of information science and technology. 42(2008), S.3-42.
    Source
    Knowledge organization. 39(2012) no.1, S.13-22
  11. Roberts, C.W.; Popping, R.: Computer-supported content analysis : some recent developments (1993) 0.02 
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    Source
    Social science computer review. 11(1993) no.3, S.283-291
  12. Hicks, C.; Rush, J.; Strong, S.: Content analysis (1977) 0.01 
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    Source
    Encyclopedia of computer science and technology, vol.6
  13. Baxendale, P.: Content analysis, specification and control (1966) 0.01 
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    Source
    Annual review of information science and technology. 1(1966), S.71-106
  14. Sharp, J.R.: Content analysis, specification, and control (1967) 0.01 
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    Source
    Annual review of information science and technology. 2(1967), S.87-122
  15. Taulbee, O.E.: Content analysis, specification, and control (1968) 0.01 
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    Annual review of information science and technology. 3(1968), S.105-136
  16. Fairthorne, R.A.: Content analysis, specification, and control (1969) 0.01 
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    Source
    Annual review of information science and technology. 4(1969), S.73-110
  17. Beghtol, C.: Stories : applications of narrative discourse analysis to issues in information storage and retrieval (1997) 0.01 
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    Abstract
    The arts, humanities, and social sciences commonly borrow concepts and methods from the sciences, but interdisciplinary borrowing seldom occurs in the opposite direction. Research on narrative discourse is relevant to problems of documentary storage and retrieval, for the arts and humanities in particular, but also for other broad areas of knowledge. This paper views the potential application of narrative discourse analysis to information storage and retrieval problems from 2 perspectives: 1) analysis and comparison of narrative documents in all disciplines may be simplified if fundamental categories that occur in narrative documents can be isolated; and 2) the possibility of subdividing the world of knowledge initially into narrative and non-narrative documents is explored with particular attention to Werlich's work on text types
  18. Naun, C.C.: Objectivity and subject access in the print library (2006) 0.01 
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    Abstract
    Librarians have inherited from the print environment a particular way of thinking about subject representation, one based on the conscious identification by librarians of appropriate subject classes and terminology. This conception has played a central role in shaping the profession's characteristic approach to upholding one of its core values: objectivity. It is argued that the social and technological roots of traditional indexing practice are closely intertwined. It is further argued that in traditional library practice objectivity is to be understood as impartiality, and reflects the mediating role that librarians have played in society. The case presented here is not a historical one based on empirical research, but rather a conceptual examination of practices that are already familiar to most librarians.
  19. Wyllie, J.: Concept indexing : the world beyond the windows (1990) 0.01 
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    Abstract
    This paper argues that the realisation of the electronic hypermedia of the future depends on integrating the technology of free text retrieval with the classification-based discipline of content analysis
  20. Hildebrandt, B.; Moratz, R.; Rickheit, G.; Sagerer, G.: Kognitive Modellierung von Sprach- und Bildverstehen (1996) 0.01 
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    Source
    Natural language processing and speech technology: Results of the 3rd KONVENS Conference, Bielefeld, October 1996. Ed.: D. Gibbon

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