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.

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.

Background: Tinnitus, known as “ringing in the ears”, is a widespread and frequently disabling hearing disorder. No pharmacological treatment exists, but clinical management techniques, such as tinnitus retraining therapy (TRT), prove effective in helping patients. Although effective, TRT is not widely offered, due to scarcity of expertise and complexity because of a high level of personalization. Within this study, a data-driven clinical decision support tool is proposed to guide clinicians in the delivery of TRT.Methods: This research proposes the formulation of data analytics models, based on supervised machine learning (ML) techniques, such as classification models and decision rules for diagnosis, and action rules for treatment to support the delivery of TRT. A knowledge-based framework for clinical decision support system (CDSS) is proposed as a UI-based Java application with embedded WEKA predictive models and Java Expert System Shell (JESS) rule engine with a pattern-matching algorithm for inference (Rete). The knowledge base is evaluated by the accuracy, coverage, and explainability of diagnostics predictions and treatment recommendations.Results: The ML methods were applied to a clinical dataset of tinnitus patients from the Tinnitus and Hyperacusis Center at Emory University School of Medicine, which describes 555 patients and 3,000 visits. The validated ML classification models for diagnosis and rules: association and actionable treatment patterns were embedded into the knowledge base of CDSS. The CDSS prototype was tested for accuracy and explainability of the decision support, with preliminary testing resulting in an average of 80% accuracy, satisfactory coverage, and explainability.Conclusions: The outcome is a validated prototype CDS system that is expected to facilitate the TRT practice.