NLP and ontology based clustering An integrated approach for optimal information extraction from social web
Social Web is a heterogeneous collection of both structured and unstructured dataprimarily composed of contents from various social networking sites, blogs, online shopping sites and much more. The knowledge extracted from such data can be valuable for accuracy of search results in light of present explosion of information exchange over social web. The extraction of information patterns from unstructured data sets available at social networking sites viz. facebook, twitter, linkedin is a challenging task as it cannot be understood by machine to robotically process the data. Also, the major data source in the form of naive users triggers the significance of filtration of the relevant results. For effectual analysis of social web contents, this paper proposes integrated NLP and ontology based clustering TVC algorithm that generates semantically meaningful concepts from the social web content. The algorithm promises to optimize the web search results and to provide accuracy in searching the well treated unstructured social web contents.
2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom)