Abstract

Objectives: The main objective of this research is to improve the topical relations by extracting the opinion targets as well as opinion words, and achieve the higher performance using word alignment model concept.

Methods: Partially Supervised Word Alignment Model (PSWAM) is used for word alignment in existing system. The Latent Dirichlet Allocation (LDA) model is used for discovering opinion word relation extraction in proposed system.

Findings: The proposed method achieves high performance in terms of sensitivity and specificity.

Application/Improvements: The proposed system is done by using Latent Dirichlet Allocation (LDA) which is used to increase the performance for number of dataset more efficiently.

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Abstract

Objectives: The main objective of this research is to achieve the quality of software code and abstract identification with high precision and recall values among diverse software program documents.

Methods: Improved Context-Sensitive Document Recovery (ICSDR) method is used to discover the more accurate abstract terms as well as produce the high quality of software code.

Findings: The proposed method achieves high performance in terms of precision, recall and accuracy.

Application/Improvements: The proposed system is done by using Improved Context-Sensitive Document Recovery (ICSDR) approcah. ICSDR method is used for identification of relevance abstract terms and improves the quality of source code significantly.

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