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A Hierarchical Clustering Approach for Dbpedia Based Contextual Information of Tweets Journal of Computer Science Original Research Paper A Hierarchical Clustering Approach for DBpedia based Contextual Information of Tweets 1Venkatesha Maravanthe, 2Prasanth Ganesh Rao, 3Anita Kanavalli, 2Deepa Shenoy Punjalkatte and 4Venugopal Kuppanna Rajuk 1Department of Computer Science and Engineering, VTU Research Resource Centre, Belagavi, India 2Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bengaluru, India 3Department of Computer Science and Engineering, Ramaiah Institute of Technology, Bengaluru, India 4Department of Computer Science and Engineering, Bangalore University, Bengaluru, India Article history Abstract: The past decade has seen a tremendous increase in the adoption of Received: 21-01-2020 Social Web leading to the generation of enormous amount of user data every Revised: 12-03-2020 day. The constant stream of tweets with an innate complex sentimental and Accepted: 21-03-2020 contextual nature makes searching for relevant information a herculean task. Multiple applications use Twitter for various domain sensitive and analytical Corresponding Authors: Venkatesha Maravanthe use-cases. This paper proposes a scalable context modeling framework for a Department of Computer set of tweets for finding two forms of metadata termed as primary and Science and Engineering, VTU extended contexts. Further, our work presents a hierarchical clustering Research Resource Centre, approach to find hidden patterns by using generated primary and extended Belagavi, India contexts. Ontologies from DBpedia are used for generating primary contexts Email: [email protected] and subsequently to find relevant extended contexts. DBpedia Spotlight in conjunction with DBpedia Ontology forms the backbone for this proposed model. We consider both twitter trend and stream data to demonstrate the application of these contextual parts of information appropriate in clustering. We also discuss the advantages of using hierarchical clustering and information obtained from cutting dendrograms. Keywords: Context Modeling, DBpedia, Extended Contexts, Hierarchical Clustering, Short Text Clustering, Twitter Data Mining Introduction of the world’s population today are active Internet users with around 1.3 billion twitter users. Recent statistics Adoption of Social Web as the platform for people to presented an average of 500 million tweets posted by exchange opinions lead to a high volume of user- 100 million active users everyday a. generated content. The availability of affordable smart Twitter works on the concept of followee and devices and easy access to the internet via wireless follower. Most content on twitter is publicly accessible. mediums such as WiFi, 4G, etc., has greatly aided this Some users however have opted for private accounts transition. Smartphone adoption, in particular, has and their activities are often not available. Abundance resulted in a further increase in the user base for social and simplified access of tweets leads to a problem of applications. The technology shift has promoted the finding the right content and mining useful information evolution of multiple social media platforms, prominent from a given user’s perspective. Human tendency has of which are Facebook, Twitter, Instagram, LinkedIn, always been to stay curious about diverse topics and etc. Users freely exchange information over these inability to find tweets suiting their interests can be platforms via text, images and videos. All the social discouraging. Hence there is a need for efficient platforms provide the necessary security and privacy frameworks that identify topics, tag contexts that features. The accessibility to the information is largely eventually help define a foundation for development of defined and controlled by the users themselves. In the context aware applications. We do find numerous Apps microblogging space, Twitter is the most popular social in popular Marketplaces working on information derived media website allowing users to assimilate concise a information directly from the genesis. More than half https://www.omnicoreagency.com/twitter-statistics/ © 2020 Venkatesha Maravanthe, Prasanth Ganesh Rao, Anita Kanavalli, Deepa Shenoy Punjalkatte and Venugopal Kuppanna Rajuk. This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license. Venkatesha Maravanthe et al . / Journal of Computer Science 2020, 16 (3): 330.343 DOI: 10.3844/jcssp.2020.330.343 by processing twitter data. Such apps include of Wikipedia categories. Gabrilovich and Markovitch recommender systems, in-app marketing, sentiment (2007) further show the versatility of concepts derived analysis, market insights, crisis management etc. from Wikipedia and proposes ‘Explicit Semantic Analysis DBpedia b knowledge-base is a crowd-sourced project (ESA)’ for computing semantic relatedness in natural developed by extracting contextual data from Wikipedia. language texts. Ramanathan and Kapoor (2009) propose a It uses Linked Open Data standard consisting of 3 billion model for creating user profiles with the help of Resource Description Framework (RDF) c triplets and Wikipedia. Framework by Genc et al . (2011) discuss provides a mechanism to query the relevant content and leveraging Wikipedia to map tweet to its semantic space, links mainly Wikipedia and Yet Another Great Ontology to calculate distance between tweets, helping better (YAGO)d categories along with many external web classification. Muñoz García et al . (2011) describe a topic pages. Multilingual support is also provided by DBpedia. recognition scheme by linking keywords to a ranked list of (Lehmann et al ., 2009; 2015) provide more insights into DBpedia resources. Authors in (Hamdan et al ., 2013) design of this framework for building the Ontology and utilized DBpedia along with WordNet and SentiWordNet maturity of the available content. They also describe the as a combination for sentiment classification. mechanisms to access information via online interfaces. User interest modeling is an important application of Hierarchical clustering is one of the cluster analysis Semantic Mining. Initially Michelson and Macskassy methods in data mining to group similar data points to (2010) use ‘Named Entity Recognition (NER)’ for clusters. Hierarchical clustering works with the help of getting entities and disambiguates leveraging cluster dissimilarity and cluster linkage which are Wikipedia for generating Twopics. Wikipedia concept explained in subsequent sections. This form of clustering linking in Lu and Lam (2012), put forth expansion of is advantageous to visualize meaningful taxonomies and user’s interest and results show better recommendation nested clusters. There are two different ways to perform using these interests. Kapanipathi et al . (2014) process a hierarchical clustering: hierarchy on ‘Wikipedia Concept Graph (WCG)’ to come up with ‘User Interest Generator’ and ‘Interest • Agglomerative (bottom-up) approach where each Hierarchy Generator’, mapping user’s primitive interests sample is a single cluster then merged form a single to Wikipedia hierarchy. Shah et al . (2018) propose cluster enrichment technique using DBpedia Ontology, • Divisive (top-down) approach which starts as a generating niche interest and inferred general interest single cluster which is broken down until one cluster and works for least active users as well. Interests of each sample is left identified using DBpedia aggregates into a user profiling framework in (Orlandi et al ., 2012). The first part of this work presents a model for Contexts or Topic identification promoted many extracting valid contexts from tweets using DBpedia recommender systems using other knowledge-bases than followed by demonstrating how clustering can be DBpedia. Abel et al . (2011) represent user modeling e applied to these contexts. Section Literature Survey with entity identification via OpenCalais . A news provides an overview of existing similar models and the recommendation system has been built on top of user applicability of their contributions to our work. Section modeling and considers the temporal dynamics of profile Problem Definition talks about the problem addressed in changes. Initiation from work (Pla Karidi, 2016) this work. Section Methodology outlines our optimized manifests to a complete recommender architecture in approach to determine contexts and clustering of the (Pla Karidi et al ., 2017) suggests both tweets and f same. Section Experimental Results presents the results followees. This system takes advantage of Alchemy API and Section Conclusions and Future Work wraps up the for deriving contexts to build a Knowledge Graph of finding while noting the scope for future enhancements. 1092 nodes and 1323 edges. A super set of 1092 concepts may not be sufficient in specific areas and Literature Survey DBpedia offers an alternative to explore. Papneja et al . (2018) propose a content recommender Usage of Wikipedia and DBpedia as a knowledge base related to user interest. DBpedia Spotlight g serves purpose for mining text data has been long part of Semantic to find mapping between domain ontology and DBpedia Mining. Bontcheva and Rout (2012) survey the various classes. Romero and Becker (2017) describe a approaches for making sense of social media data streams. classification framework, taking advantage of DBpedia for Authors highlight the importance of Linked Open Data enriching semantic features. DBpedia spotlight connects resources, entity linking with Wikipedia
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