Community Detection in Collaborative Tagging Systems

Papadopoulos, Symeon; Vakali, Athena; Kompatsiaris, Ioannis

doi:10.1007/978-3-642-19047-6_5

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…The performance measure taken under considerations are computational complexity of various algorithms and their memory requirements. Symeon et al [16] further discusses various applications using community detection with respect to social media analysis including Topic detection in collaborative tagging systems [15,17], User profiling [6], Photo clustering [12], Event detection [20], etc.…”

Section: Advancements In Community Detection and Sentiment Analysismentioning

confidence: 99%

A Review of Common Approaches to Sentiment Analysis and Community Detection

Bhatnagar¹,

Dixit²,

Prasad³

2020

IJCA

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Sentiment analysis and community detection are two very active fields of research in computer science. They are both intimately linked to the modern phenomenon of social media, and can be very useful for extracting valuable information from a large corpus of social media posts. In this paper, we review the basic concepts of both fields and outline some of the algorithms and approaches that have been successfully applied. Finally, we take a look at the instances where both have been applied together.

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Section: Advancements In Community Detection and Sentiment Analysismentioning

confidence: 99%

A Review of Common Approaches to Sentiment Analysis and Community Detection

Bhatnagar¹,

Dixit²,

Prasad³

2020

IJCA

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“…Tag community : tag community detection is to find out groups of tags that are either semantically close to each other or used in the same context (Papadopoulos et al ., 2011).…”

Section: Tag Management Features Provided By the Folksonomy Applicationsmentioning

confidence: 99%

Quality-protected folksonomy maintenance approaches: a brief survey

Jabeen

Khusro

2015

The Knowledge Engineering Review

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Folksonomy gives liberty to its users to freely assign chosen keywords as tags, and this is the main reason behind its popularity. Apart from freedom, this system also reflects the collective intelligence of the crowd. However, this freedom and liberty can degrade quality of the folksonomy. It is required that quality of the folksonomy must remain consistently excellent and does not degrade with the passage of time. This is a survey paper, in which we present a brief survey of the research efforts intended to maintain a quality-protected folksonomy. We have organized our paper by looking at the problem from four aspects namely selection of quality tags, tag management features provided by folksonomy applications, folksonomy cleaning and interoperability of tags across platforms. We conclude our review with some of the interesting research topics, which need to be explored further. Our conclusion will be relevant and beneficial for engineers and designers who aim to design and maintain a quality-protected folksonomy.

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“…Finally, the identified cores are expanded by maximizing a local measure of modularity [61] in order to increase the number of nodes that are assigned to communities and to allow for overlap among communities. The scheme is described in detail in [62] and its three steps are briefly explained below:…”

Section: Community Detection On Tag Graphsmentioning

confidence: 99%

“…Qualitative evaluation was based on subjective assessment of the derived tag communities and an implicit evaluation by using the derived clusters for tag recommendation and measuring the achieved performance on historical tagging data from three different tagging sources (Delicious, Flickr, and BibSonomy). Complete results are reported in detail in [62]. The process of extracting tag clusters from massive user tagging leads to promising results, however it is solely based on the statics of tag usage.…”

Section: Fig 41 Tag Community Around Tag "Computers"mentioning

confidence: 99%