An effective approach of intrinsic and extrinsic domain relevance technique for feature extraction in opinion mining

Opinion mining, also known as sentiment analysis, refers to the method of identifying and extracting subjective information from the source materials by the use of natural language processing, text analysis and computational linguistics. It has gained immense amount of importance in recent times due to the growing number of blogs, forums and other social networks which contain huge amount of opinions. Feature extraction is an important factor in opinion mining which refers to the method of extracting those properties on which the opinions are based on. There is a method for identifying features based on Intrinsic and Extrinsic Domain Relevance (IEDR) which exploits the difference in opinion feature's statistics across two corpora. The approach includes syntactic rules to process the review sentences and a well known and generalized weight equation with a numerical statistic known as Term Frequency-Inverse Document Frequency to calculate domain relevance which often fails to identify many of the legitimate features. So in this paper, we propose an effective approach of this IEDR technique for the purpose of feature extraction. Our proposed approach includes a handful of extended syntactic rules to process review sentences. It also includes optimization in calculation of domain relevance with the modification of weight equation. To verify our proposed approach, we have applied it on two real-world review corpora along with the existing IEDR approach. Our proposed approach exhibits a remarkable improvement in performance for finding opinion features outperforming the currently existing IEDR method.