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  • × theme_ss:"Inhaltsanalyse"
  1. Diao, J.: Conceptualizations of catalogers' judgment through content analysis : a preliminary investigation (2018) 0.00 
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  2. Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019) 0.00 
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    Abstract
    This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.
    Type
    a
  3. Hoover, L.: ¬A beginners' guide for subject analysis of theses and dissertations in the hard sciences (2005) 0.00 
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    Abstract
    This guide, for beginning catalogers with humanities or social sciences backgrounds, provides assistance in subject analysis (based on Library of Congress Subject Headings) of theses and dissertations (T/Ds) that are produced by graduate students in university departments in the hard sciences (physical sciences and engineering). It is aimed at those who have had little or no experience in cataloging, especially of this type of material, and for those who desire to supplement local mentoring resources for subject analysis in the hard sciences. Theses and dissertations from these departments present a special challenge because they are the results of current research representing specific new concepts with which the cataloger may not be familiar. In fact, subject headings often have not yet been created for the specific concept(s) being researched. Additionally, T/D authors often use jargon/terminology specific to their department. Catalogers often have many other duties in addition to subject analysis of T/Ds in the hard sciences, yet they desire to provide optimal access through accurate, thorough subject analysis. Tips are provided for determining the content of the T/D, strategic searches on WorldCat for possible subject headings, evaluating the relevancy of these subject headings for final selection, and selecting appropriate subdivisions where needed. Lists of basic reference resources are also provided.
    Type
    a
  4. Thelwall, M.; Buckley, K.; Paltoglou, G.: Sentiment strength detection for the social web (2012) 0.00 
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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.
    Type
    a
  5. Saif, H.; He, Y.; Fernandez, M.; Alani, H.: Contextual semantics for sentiment analysis of Twitter (2016) 0.00 
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    Abstract
    Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.
    Type
    a
  6. Bednarek, M.: Intellectual access to pictorial information (1993) 0.00 
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    Abstract
    Visual materials represent a significantly different type of communication to textual materials and therefore present distinct challenges for the process of retrieval, especially if by retireval we mean intellectual access to the content of images. This paper outlines the special characteristics of visual materials, focusing on their pontential complexity and subjectivity, and the methods used and explored for gaining access to visual materials as reported in the literature. It concludes that methods of access to visual materials are dominated by the relative mature systems developed for textual materials and that access methods based on visual communication are still largely in the developmental or prototype stage. Although reported research on user requirements in the retrieval of visual information is noticeably lacking, the results of at least one study indicate that the visually-based retrieval methods of structured and unstructered browsing seem to be preferred for visula materials and that effective retrieval methods are ultimately related to characteristics of the enquirer and the visual information sought
    Type
    a
  7. Chu, C.M.; O'Brien, A.: Subject analysis : the critical first stage in indexing (1993) 0.00 
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  8. Miene, A.; Hermes, T.; Ioannidis, G.: Wie kommt das Bild in die Datenbank? : Inhaltsbasierte Analyse von Bildern und Videos (2002) 0.00 
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  9. Rorissa, A.; Iyer, H.: Theories of cognition and image categorization : what category labels reveal about basic level theory (2008) 0.00 
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  10. Mochmann. E.: Inhaltsanalyse in den Sozialwissenschaften (1985) 0.00 
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  11. Todd, R.J.: Subject access: what's it all about? : some research findings (1993) 0.00 
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  12. Svenonius, E.: Access to nonbook materials : the limits of subject indexing for visual and aural languages (1994) 0.00 
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  13. Nohr, H.: Inhaltsanalyse (1999) 0.00 
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  14. Andersen, J.: ¬The concept of genre : when, how, and why? (2001) 0.00 
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  15. Xie, H.; Li, X.; Wang, T.; Lau, R.Y.K.; Wong, T.-L.; Chen, L.; Wang, F.L.; Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy (2016) 0.00 
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    Abstract
    In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.
    Type
    a
  16. Pozzi de Sousa, B.; Ortega, C.D.: Aspects regarding the notion of subject in the context of different theoretical trends : teaching approaches in Brazil (2018) 0.00 
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  17. Hjoerland, B.: Subject (of documents) (2016) 0.00 
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    Abstract
    This article presents and discusses the concept "subject" or subject matter (of documents) as it has been examined in library and information science (LIS) for more than 100 years. Different theoretical positions are outlined and it is found that the most important distinction is between document-oriented views versus request-oriented views. The document-oriented view conceive subject as something inherent in documents, whereas the request-oriented view (or the policy based view) understand subject as an attribution made to documents in order to facilitate certain uses of them. Related concepts such as concepts, aboutness, topic, isness and ofness are also briefly presented. The conclusion is that the most fruitful way of defining "subject" (of a document) is the documents informative or epistemological potentials, that is, the documents potentials of informing users and advance the development of knowledge.
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    a
  18. Jörgensen, C.: ¬The applicability of selected classification systems to image attributes (1996) 0.00 
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  19. Hutchins, W.J.: ¬The concept of 'aboutness' in subject indexing (1978) 0.00 
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  20. Beghtol, C.: Stories : applications of narrative discourse analysis to issues in information storage and retrieval (1997) 0.00 
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Themes