What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Naive Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages

Web classification has been attempted through many different technologies. In this study we concentrate on the comparison of Neural Networks (NN), Naïve Bayes (NB) and Decision Tree (DT) classifiers for the automatic analysis and classification of attribute data from training course web pages. We introduce an enhanced NB classifier and run the same data sample through the DT and NN classifiers to determine the success rate of our classifier in the training courses domain. This research shows that our enhanced NB classifier not only outperforms the traditional NB classifier, but also performs similarly as good, if not better, than some more popular, rival techniques. This paper also shows that, overall, our NB classifier is the best choice for the training courses domain, achieving an impressive F-Measure value of over 97%, despite it being trained with fewer samples than any of the classification systems we have encountered

Text document pre-processing using the Bayes formula for classification based on the vector space model

This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent the document, the text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM) and Self-Organizing Map (SOM) can then be used to classify the documents on a multi-dimensional level, thus improving on the results obtained using only the highest probability to classify the document, such as that achieved by implementing the naïve Bayes classifier by itself. The effects of an inadvertent dimensionality reduction can be overcome using these algorithms. We compare the performance of these classifiers for high dimensional data

Text document pre-processing using the Bayes formula for classification based on the vector space model

This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent the document, the text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM) and Self-Organizing Map (SOM) can then be used to classify the documents on a multi-dimensional level, thus improving on the results obtained using only the highest probability to classify the document, such as that achieved by implementing the naïve Bayes classifier by itself. The effects of an inadvertent dimensionality reduction can be overcome using these algorithms. We compare the performance of these classifiers for high dimensional data