Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy

Haoran XIE, Xiaodong LI*, Tao WANG, Raymond Y.K. LAU, Tak-Lam WONG, Li CHEN, Fu Lee WANG, Qing LI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)

58 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)61-72
Number of pages12
JournalInformation Processing and Management
Volume52
Issue number1
Early online date1 Apr 2015
DOIs
Publication statusPublished - Jan 2016
Externally publishedYes

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Bibliographical note

The work described in this paper was substantially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14).

Keywords

  • Folksonomy
  • Personalized search
  • Sentiment
  • Social media
  • User profiling

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