Opinion Mining Using Normal Discriminant Piecewise Regressive (NDPR) Sentiment Classification Technique

Abstract

Sentiment analysis through opinion mining is determined through significant and growing interest for many industries including hotel, tourism, educations and so on. Sentiment analysis includes design of the system to search the user opinions in blog posts, comments, reviews or tweets regarding the product, policy or area. Many researchers carried out their research on opinion mining to identify the polarity of the statements. But the main problem during opinion mining is that the words chosen do not solve attribute relevancy and could not classify the positive and negative usage of uncertain terms. In order to address these problems, normal discriminant piecewise regressive (NDPR) sentiment classification technique is introduced. NDPR technique perform three processes, namely, pre-processing, feature extraction and classification to improve the accuracy level through forming classes (i.e., positive, neutral and negative) based on the extracted words from user review comments. Initially, NDPR technique performs the data pre-processing task for stemming and removing the stop words from review statements to reduce the file size that in turn improves the efficiency. After that, normal discriminant feature extraction process is carried out in NDPR technique to extract the opinion word from the review statements sent by reviewers. The related opinion words are systematized for their semantic equivalence of sentiment based on extracted word. This helps to reduce the time consumption to extract the opinions from reviewers. Finally, piecewise regressive sentiment classification (PRSC) process is carried out in NDPR technique to analyse the semantic opinion words for evaluating the sentiment class label. The sentiment class labels are categorized into positive, neutral and negative sentiments with the user review comments. This in turn helps to reduce the time consumption to extract the opinions from reviewers and to improve the review detection accuracy. The performance evaluation of NDPR technique is carried out with standard benchmark datasets of consumer product and services reviews extracted. The parameters used in evaluation are number of customer review words, accuracy, time complexity (TC) and false positive rate. Experimental analysis shows that NDPR technique reduces the time to extract the opinions from reviewers and false positive rate.