Visible to the public Recommendation of Keywords Using Swarm Intelligence Techniques

TitleRecommendation of Keywords Using Swarm Intelligence Techniques
Publication TypeConference Paper
Year of Publication2016
AuthorsSheeba, J. I., Devaneyan, S. Pradeep
Conference NameProceedings of the International Conference on Informatics and Analytics
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4756-3
Keywordscomposability, Conversations transcripts, Firefly, Human Behavior, keyword extraction, Metrics, pubcrawl, Scalability, Stochastic Diffusion Search, swarm intelligence, text analytics, text mining
Abstract

Text mining has developed and emerged as an essential tool for revealing the hidden value in the data. Text mining is an emerging technique for companies around the world and suitable for large enduring analyses and discrete investigations. Since there is a need to track disrupting technologies, explore internal knowledge bases or review enormous data sets. Most of the information produced due to conversation transcripts is an unstructured format. These data have ambiguity, redundancy, duplications, typological errors and many more. The processing and analysis of these unstructured data are difficult task. But, there are several techniques in text mining are available to extract keywords from these unstructured conversation transcripts. Keyword Extraction is the process of examining the most significant word in the context which helps to take decisions in a much faster manner. The main objective of the proposed work is extracting the keywords from meeting transcripts by using the Swarm Intelligence (SI) techniques. Here Stochastic Diffusion Search (SDS) algorithm is used for keyword extraction and Firefly algorithm used for clustering. These techniques will be implemented for an extensive range of optimization problems and produced better results when compared with existing technique.

URLhttp://doi.acm.org/10.1145/2980258.2980286
DOI10.1145/2980258.2980286
Citation Keysheeba_recommendation_2016