NLP-Based customer loyalty improvement recommender system (CLIRS2)

Structured data on customer feedback is becoming more costly and timely to collect and organize. On the other hand, unstructured opinionated data, e.g., in the form of free-text comments, is proliferating and available on public websites, such as social media websites, blogs, forums, and websites that provide recommendations. This research proposes a novel method to develop a knowledge-based recommender system from unstructured (text) data. The method is based on applying an opinion mining algorithm, extracting aspect-based sentiment score per text item, and transforming text into a structured form. An action rule mining algorithm is applied to the data table constructed from sentiment mining. The proposed application of the method is the problem of improving customer satisfaction ratings. The results obtained from the dataset of customer comments related to the repair services were evaluated with accuracy and coverage. Further, the results were incorporated into the framework of a web-based user-friendly recommender system to advise the business on how to maximally increase their profits by introducing minimal sets of changes in their service. Experiments and evaluation results from comparing the structured data-based version of the system CLIRS (Customer Loyalty Improvement Recommender System) with the unstructured data-based version of the system (CLIRS2) are provided

Contemporary Art Authentication With Large-Scale Classification

Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible as provenance can be obscured or lost through anonymity, forgery, gifting, or theft of artwork. This paper presents an image-only art authentication attribute marker of contemporary art paintings for a very large number of artists. The experiments in this paper demonstrate that it is possible to use ML-generated models to authenticate contemporary art from 2368 to 100 artists with an accuracy of 48.97% to 91.23%, respectively. This is the largest effort for image-only art authentication to date, with respect to the number of artists involved and the accuracy of authentication

Impact of credibility on opinion analysis in social media

© 2018 IOS Press. All rights reserved. In conjunction with the rapid growth and adoption of social media, people are more and more willing to share their personal experiences and opinions about products and/or services with the community. Opinions could be the basis of developing systems that would advise future users on how to proceed with any purchase without risking any disappointment. Unfortunately, opinions are not always genuine due to for instance, biased users as well as mixed feedback coming from the same users (i.e., multi-identity). This paper presents an approach for opinion analysis using credibility as a decisive criterion for supporting future users make sound decisions. The effectiveness of this approach has been tested using opinions posted on Twitter

Action rules discovery, a new simplified strategy

A new strategy for discovering action rules (or interventions) is presented in this paper. The current methods [14], [12], [8] require to discover classification rules before any action rule can be constructed from them. Several definitions of action rules [8], [13], [9], [3] have been proposed. They differ in the generality of their classification parts but they are always constructed from certain pairs of classification rules. Our new strategy defines the classification part of an action rule in a unique way. Also, action rules are constructed from single classification rules. We show how to compute their confidence and support. Action rules are used to reclassify objects. In this paper, we propose a method for measuring the level of reclassification freedom for objects in a decision system