Affiliations: [a] Department of Computer Science, San Jose State University, 1 Washington Sq, San Jose, CA 95192, USA. E-mail: katarzyna.tarnowska@sjsu.edu | [b] Department of Computer Science, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA | [c] Polish-Japanese Academy of Information Technology, 02-008 Warsaw, Poland. E-mail: ras@uncc.edu
Abstract: This paper presents an application of sentiment analysis on customer feedback data in the area of heavy equipment repair services. Sentiment analysis is used as a part of a framework for text mining-based Customer Loyalty Improvement Recommender System (CLIRS). In order to provide business users of the system with accurate predictions for customer satisfaction metrics, the original algorithm for the opinion mining needed to be improved. The paper presents the background of the proposed approach, the current techniques used to mine text data and existing applications of sentiment analysis. We propose an aspect-based, taxonomy-driven approach for customized sentiment analysis. The contribution of this paper is the implementation and evaluation of the proposed methods that improve the accuracy and coverage of the opinion mining algorithm. The improvements are illustrated with examples covered by the algorithm in the customer dataset. The application of the proposed methods resulted in increasing the algorithm’s accuracy from 92% to 96%, and coverage from 36% to 48%. This research is an attempt to handle well-known issues in natural language processing that are currently not handled by text mining algorithms, such as ambiguity and context, opinionated verbs/nouns, subject recognition from pronouns. This is significant because the proposed techniques are generalizable to any application that uses sentiment analysis algorithm.
