Aspect-based classification of product reviews using Hadoop framework. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Rodrigues, Anisha P.; Chiplunkar, Niranjan N.; Fernandes, Roshan
• Editors: Robnik-Šikonja, Marko
• Is Part Of: Cogent engineering. Volume 7:Issue 1(2020)
• Publication Date: 2020-01-01
• Subjects: sentiment analysis -- big data -- Hadoop -- support vector machine -- Naïve Bayes classifier; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2020.1810862
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: The advancement of e-commerce along with the quick development of product review discussion in the most recent decade, an enormous measure of sentiment data or reviews are produced which made it practically difficult for a customer to take an educated buy choice. A good number of customers share their conclusions about the product during these days. These reviews have an important role in customers' purchase-decision process. For a popular product there may be hundreds or thousands of reviews. This is difficult for a potential customer to go to each of them to make an educated choice on whether to buy the product or not. Also, this is difficult for manufacturer of the product to follow-up and to manage client opinions. In this scenario, the aspects-based sentimental analysis helps in analyzing the reviews and categorizing them into appropriate aspects. Aspect or feature refers to attributes or qualities of a product. The proposed work begins with collecting reviews from online shopping websites, identifying aspects and classifying opinion orientation of aspects with different sentiment analysis techniques using Hadoop framework. This paper proposes a new pattern-based method for aspect extraction and sentiment analysis which gives an accuracy in the range of 72 ~ 75%. The proposed work is implemented on Hadoop MapReduce framework and the results show that Hadoop Multi-Node cluster set up performs aspect level sentiment analysis in a shorter time compared to traditional techniques.