A multi-level text classifier for feedback analysis using tweets to enhance product performance. (2015)

• Record Type: Journal Article
• Authors: Balusamy, Balamurugan; Murali, Thusitha; Thangavelu, Aishwarya; Krishna, P. Venkata
• Is Part Of: International journal of electronic marketing and retailing. Volume 6:Number 4(2015)
• Page Start: 315
• Page End: 338
• Publication Date: 2015
• Subjects: big data -- three-level text classifiers -- principal components analysis -- PCA -- support vector machines -- SVM -- conditional random fields -- CRF -- feedback classification -- tweets -- product performance -- sentiment analysis -- Twitter -- social media; Telemarketing -- Periodicals; Internet marketing -- Periodicals; Electronic commerce -- Periodicals; 658.872
• Languages: English
• ISSNs: 1741-1025

Abstract

Big Data refers to the collection and storage of the enormous amount of data which is heterogeneous in nature. Data analysis is quite complex due to its enormous volume and its high generation speed. Big Data has many business applications such as in promotion, marketing either financially or by supporting in decision making. One such application is the sentiment analysis that paves the way for the business analysts to know the positive or negative impact over the product based on the tweets by the people. We propose a three-level text classifier with the first level as principal components analysis (PCA) followed by the support vector machine (SVM) and the conditional random fields (CRF) as the second and third level using the tweets collected. This feedback analyser would promote the sales of the product due to its high accuracy in feedback classification.