Browsing by Author "Mallya, D."
Now showing 1 - 4 of 4
- Results Per Page
- Sort Options
Item Ontology based approach for event detection in twitter datastreams(2015) Kaushik, R.; Apoorva, Chandra, S.; Mallya, D.; Chaitanya, J.N.V.K.; Sowmya, Kamath S.In this paper, we present a system that attempts to interpret relations in social media data based on automatically constructed dataset-specific ontology. Twitter data pertaining to the real world events such as the launch of products and the buzz generated by it, among the users of Twitter for developing a prototype of the system. Twitter data is filtered using certain tag-words which are used to build an ontology, based on extracted entities. Wikipedia data on the entities are collected and processed semantically to retrieve inherent relations and properties. The system uses these results to discover related entities and the relationships between them. We present the results of experiments to show how the system was able to effectively construct the ontology and discover inherent relationships between the entities belonging to two different datasets. � 2015 IEEE.Item Ontology based approach for event detection in twitter datastreams(Institute of Electrical and Electronics Engineers Inc., 2015) Kaushik, R.; Apoorva Chandra, S.; Mallya, D.; Chaitanya, J.N.V.K.; Kamath S․, S.In this paper, we present a system that attempts to interpret relations in social media data based on automatically constructed dataset-specific ontology. Twitter data pertaining to the real world events such as the launch of products and the buzz generated by it, among the users of Twitter for developing a prototype of the system. Twitter data is filtered using certain tag-words which are used to build an ontology, based on extracted entities. Wikipedia data on the entities are collected and processed semantically to retrieve inherent relations and properties. The system uses these results to discover related entities and the relationships between them. We present the results of experiments to show how the system was able to effectively construct the ontology and discover inherent relationships between the entities belonging to two different datasets. © 2015 IEEE.Item Sociopedia: An interactive system for event detection and trend analysis for twitter data(2016) Kaushik, R.; Apoorva, Chandra, S.; Mallya, D.; Chaitanya, J.N.V.K.; Sowmya, Kamath S.The emergence of social media has resulted in the generation of highly versatile and high volume data. Most web search engines return a set of links or web documents as a result of a query, without any interpretation of the results to identify relations in a social sense. In the work presented in this paper, we attempt to create a search engine for social media datastreams, that can interpret inherent relations within tweets, using an ontology built from the tweet dataset itself. The main aim is to analyze evolving social media trends and providing analytics regarding certain real world events, that being new product launches, in our case. Once the tweet dataset is pre-processed to extract relevant entities, Wiki data about these entities is also extracted. It is semantically parsed to retrieve relations between the entities and their properties. Further, we perform various experiments for event detection and trend analysis in terms of representative tweets, key entities and tweet volume, that also provide additional insight into the domain. � Springer India 2016.Item Sociopedia: An interactive system for event detection and trend analysis for twitter data(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2016) Kaushik, R.; Apoorva Chandra, S.; Mallya, D.; Chaitanya, J.N.V.K.; Kamath S․, S.The emergence of social media has resulted in the generation of highly versatile and high volume data. Most web search engines return a set of links or web documents as a result of a query, without any interpretation of the results to identify relations in a social sense. In the work presented in this paper, we attempt to create a search engine for social media datastreams, that can interpret inherent relations within tweets, using an ontology built from the tweet dataset itself. The main aim is to analyze evolving social media trends and providing analytics regarding certain real world events, that being new product launches, in our case. Once the tweet dataset is pre-processed to extract relevant entities, Wiki data about these entities is also extracted. It is semantically parsed to retrieve relations between the entities and their properties. Further, we perform various experiments for event detection and trend analysis in terms of representative tweets, key entities and tweet volume, that also provide additional insight into the domain. © Springer India 2016.