Social Tagging and its Trend: A Review

Presented by:Er. Priyanka Pradhan

M.Tech ScholarSrmscet

Content

• Introduction• Collaborative Tagging• Four techniques of taggingØ Bayesian NetworkØ K-Nearest NeighborØ Rule induction

• Conclusion

1. INTRODUCTION

• Social Tagging, also known as Social Annotation or Collaborative Tagging. Social annotation is one of the most diffused and popular services available online that allows users to annotate resources with free-form tags. The main purpose of social annotation is to loosely classify resources based on end-user’s feedback, expressed in the form of free-text labels (i.e., tags).

Collaborative tagging is one of the most diffused and popular services available online. First provided by social bookmarking sites only for example, Delicious (http://delicious.comby nearly any type of social web application, and it is used to annotate any kind of online and offline resources.

2. COLLABORATIVE TAGGING

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2.1. Four techniques of tagging:

2.1.1. Bayesian Network:

• The Bayesian network tells us dependencies of probabilities on external interventions. Bayesian network model use to handle uncertainty. This is also known as directed acyclic graphical model that represents the probabilistic dependencies among random variables.

2.1.2. K-Nearest Neighbor:

●  K-mean is a highly adaptive & more efficient algorithm based on iteration to form the clusters. K-Mean clustering is based on user tag. It iteratively calculates the cluster centroids and reassigns each document to the closest cluster until no document can be reassigned. Taking into account the social annotations, the web documents can be clustered with K-means based on the following models:

Contd…

• 2.1.2.1.Tag vector model• 2.1.2.2.(Word+Tag) vector model• 2.1.2.3.Word vector + Tag vector model

2.1.3Rule induction:

• The rule induction method use the symbolic rules that inserts the SGML () tags into text. These additional rules are used for correcting mistakes. The symbolic rules which insert the SGML tags are inducted in two steps. These two rules are:

Contd…

• 2.1.3.1.Tagging rules• 2.1.3.2. Correctionrules

Fig.1Graph on various tagging keywords search (as per Google trends)

Graph in fig.2 represents the concern of tagging among people over worldwide. New Zealand, Australia & Singapore are the top 3 countries

that have interest in tagging (as per the Google trends).

Graph in fig.3 reflects the using trends of tagging techniques from 2005 to 2014 around the world (as per the Google trends). Graph is generated for tagging techniques: Bayesian

network, rule induction, linear classifier & k-means.

 

 

 Conclusion: • This paper concludes that within a decade the 

tagging feature gets the spotlight worldwide. Many websites are using the tagging for classifying the related data with a tag. This label (i.e. tag) on similar data, helps in managing searched the data more efficiently. feature &fascinated the developers & users from the recent past. The k-means technique is most popular for tagging.

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