Text Mining Techniques for Intelligent Grievances Handling System: WECARE Project Improvements in EgyptAir

Abstract

The current work provides quick responding and minimize the required time of processing of the incoming grievances by using automated categorization that analyses the English text contents and predict the category. This work built a model by text mining and NLP processing to extract the useful information from customer grievances data to be used as a guideline to air transport industry. A customer grievances' system in EGYPTAIR called WECARE has had large feeds of data which can be collected in data sets through various channels such as e-mail, website or mobile Apps. Then the incoming data sets are analyzed and assessed by organization's staff then it is assigned to related department through manual classification. Finally, it provides proposed solution for the issue. Thence grievances categorization that handled manually is time consuming process. So, this work decided a model to improve WECARE system in Egypt Airlines. Classification based data mining Techniques are used to identify data into groups of categories across the variable touch points. The system has 166 categories of problems, but for experimental purposes we decided to study six categories only. We have applied four commonly used classifiers, namely, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Naïve Bayesian and Decision Tree on our data set to classify the grievances data set then selecting the best of them to be the candidate grievances classifier in enhanced WECARE system. Among four classifiers applied on the dataset, KNN achieved the highest average accuracy (97.5%) with acceptable running time. Also, the work is extended to make hint to the system user, about how to solve this grievance issue based on previous issues saved in Knowledge Base (KB). Several experiments were conducted to test solution hint module by changing similarity score. The benefits of performing a thorough analysis of problems include better understanding of service performance.

Assessing the Influence Level of Food Safety Public Opinion with Unbalanced Samples Using Ensemble Machine Learning

“…When text underwent feature extraction and turned into numerical features, various machine learning methods can be used to classify text. Al-Tabbakh et al use support vector machine, k-nearest neighbors, naive Bayes, and decision trees to classify the same text collection, whose results show that k-nearest neighbors perform the best in the experiment [19]. In contrast, deep learning algorithms do not require text feature extraction [20].…”