A graph based keyword extraction model using collective node weight

In the recent times, a huge amount of text is being generated for social purposes on twitter social networking site. Summarizing and analysing of twitter content is an important task as it benefits many applications such as information retrieval, automatic indexing, automatic classification, automatic clustering, automatic filtering etc. One of the most important tasks in analyzing tweets is automatic keyword extraction. There are some graph based approaches for keyword extraction which determine keywords only based on centrality measure. However, the importance of a keyword in twitter depends on various parameters such as frequency, centrality, position and strength of neighbors of the keyword. Therefore, this paper proposes a novel unsupervised graph based keyword extraction method called Keyword Extraction using Collective Node Weight (KECNW) which determines the importance of a keyword by collectively taking various influencing parameters. The KECNW is based on Node Edge rank centrality with node weight depending on various parameters. The model is validated with five datasets: Uri Attack, American Election, Harry Potter, IPL and Donald Trump. The result of KECMW is compared with three existing models. It is observed from the experimental results that the proposed method is far better than the others. The performances are shown in terms of precision, recall and F-measure.