Search (5 results, page 1 of 1)

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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.

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Abstract

Author-selected keywords have been widely utilized for indexing, information retrieval, bibliometrics and knowledge organization in previous studies. However, few studies exist con-cerning how author-selected keywords function semantically in scientific manuscripts. In this paper, we investigated this problem from the perspective of term function (TF) by devising indica-tors of the diversity and symmetry of keyword term functions in papers, as well as the intensity of individual term functions in papers. The data obtained from the whole Journal of Informetrics(JOI) were manually processed by an annotation scheme of key-word term functions, including "research topic," "research method," "research object," "research area," "data" and "others," based on empirical work in content analysis. The results show, quantitatively, that the diversity of keyword term function de-creases, and the irregularity increases with the number of author-selected keywords in a paper. Moreover, the distribution of the intensity of individual keyword term function indicated that no significant difference exists between the ranking of the five term functions with the increase of the number of author-selected keywords (i.e., "research topic" > "research method" > "research object" > "research area" > "data"). The findings indicate that precise keyword related research must take into account the dis-tinct types of author-selected keywords.

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Date

28. 1.2006 19:21:22

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Date

15. 3.2019 18:55:29

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Date

20. 1.2015 18:30:22