Net Promoter Sentiment Classifier Using OHPL-All
Document Type
Article
Publication Date
12-2019
Abstract
Net Promotor Score is an important business measurement process where customers are surveyed and asked to rate their likelihood of recommending the company’s products and/or services. In many applications, customers are asked to respond on an 11-point ordinal scale of 0 to 10. In developing the score, the data are reformulated into a labelled 3 class scale (0-6: Detractor, 7-8: Passive and 9-10: Promoter). [1] Many companies that choose to use Net Promoter Score as a core management metric integrate the measurement into all phases of the company and seek every opportunity to assess company performance in terms of likelihood to promote the company. In addition to a variety of survey opportunities, the ability to score comments in survey, social media and blogs with promoter rating may provide an additional valuable source of business insight. Even on a three-point scale, Net Promoter is an ordinal classification problem. A number of successful algorithms, that develop ordinal classifiers have been developed. [2] None of the top performing classifiers can be used for applications like text classification or image classification, since they don’t employ deep learning. Any appropriate strategy must utilize the ordering information of classes without imposing a strong continuous assumption or fixed spacing assumption on the ordinal classes. In this paper, we use a novel Deep Learning methodology called OHPLnet (Ordinal Hyperplane Loss Network) that is specifically designed for data with ordinal classes. [3] The algorithm is used to develop predictions of the eleven classes, that may be used in the standard Net Promoter Score generation process.
Journal
Proceedings of 2019 IEEE International Conference on Big Data