International Journal of Management and Applied Science (IJMAS)
current issues
Volume-5,Issue-1  ( Jan, 2019 )
Past issues
  1. Volume-5,Issue-1  ( Jan, 2019 )
  2. Volume-4,Issue-12  ( Dec, 2018 )
  3. Volume-4,Issue-11  ( Nov, 2018 )
  4. Volume-4,Issue-10  ( Oct, 2018 )
  5. Volume-4,Issue-9  ( Sep, 2018 )
  6. Volume-4,Issue-8  ( Aug, 2018 )
  7. Volume-4,Issue-7  ( Jul, 2018 )
  8. Volume-4,Issue-6  ( Jun, 2018 )
  9. Volume-4,Issue-5  ( May, 2018 )
  10. Volume-4,Issue-4  ( Apr, 2018 )

Statistics report
Mar. 2019
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 59
Paper Published : 3874
No. of Authors : 7966
  Journal Paper




Paper Title :
An Efficient IEDR- Intrinsic/ Extrinsic Domain Relevance approach for Product Feature Ranking

Author :Madhuri V. Shirsat, Nilesh S. Vani

Article Citation :Madhuri V. Shirsat ,Nilesh S. Vani , (2018 ) " An Efficient IEDR- Intrinsic/ Extrinsic Domain Relevance approach for Product Feature Ranking " , International Journal of Management and Applied Science (IJMAS) , pp. 30-33, Volume-4,Issue-10

Abstract : In research market, sentiment analysis plays an important role. As per review much more focus on online-review communities with different polarities, which is not informative as compared to rating scheme. So in this paper more concentrate on proposed system relates to follow IEDR algorithm to extract the opinion features and then by using probabilistic aspect ranking algorithm, rank the product using numeric scores. So by simulation results it is clear that proposed IEDR system shows large number of dataset by accepting various types of product reviews. Also identify opinion features through online review by examining difference in domain specific and domain independent corpus. The results of existing IDR algorithm are compared with proposed IEDR. The results from Precision, Recall and F-measure are indicates as compared to the existing system all results are improved through proposed system. In future, need of extended approach to identify opinion features like non-noun features, infrequent features, as well as implicit features. Keywords - Opinion Mining, Intrinsic and Extrinsic Domain, Domain relevance, Sentiments.

Type : Research paper


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