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

Personalized Recommendation Model: An Online Comment Sentiment Based Analysis

Traditional recommendation algorithms measure users’ online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Users’ reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the fine-grained analysis of emotions expressed in users’ online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts feature-sentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user feature-sentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy

The Role of Text Pre-processing in Sentiment Analysis

It is challenging to understand the latest trends and summarise the state or general opinions about products due to the big diversity and size of social media data, and this creates the need of automated and real time opinion extraction and mining. Mining online opinion is a form of sentiment analysis that is treated as a difficult text classification task. In this paper, we explore the role of text pre-processing in sentiment analysis, and report on experimental results that demonstrate that with appropriate feature selection and representation, sentiment analysis accuracies using support vector machines (SVM) in this area may be significantly improved. The level of accuracy achieved is shown to be comparable to the ones achieved in topic categorisation although sentiment analysis is considered to be a much harder problem in the literature

A Decision Method for Online Purchases Considering Dynamic Information Preference Based on Sentiment Orientation Classification and Discrete DIFWA Operators

© 2013 IEEE. Online reviews are crucial for evaluating product features and supporting consumers' purchase decisions. However, as a result of online buying behaviors, consumer habits, and discrete dynamic distribution characteristics of online reviews, and consumers typically randomly choose a limited number of reviews from discrete time frames among all reviews and give more weight to recent review information and less weight to earlier information to support their online purchase decisions; moreover, existing studies have ignored the discrete random dynamic characteristics and dynamic information preferences of consumers. To address this issue, this paper proposes a method based on sentiment orientation classification and discrete DIFWA (DDIFWA) operators for online purchase decisions considering dynamic information preferences. In this method, we transformed review texts from original discrete time slices to discrete random features, extracted product features based on the constructed feature and sentiment dictionaries, and matched pairs of features and sentiment phrases in the dictionaries. We subsequently employed three types of semantic orientation by defining semantic rules to extract the product features of each review. Of note, the semantic orientations were transformed from discrete time to semantic intuitionistic fuzzy numbers and semantic intuitionistic fuzzy information matrixes. Furthermore, we proposed two DDIFWA operators to aggregate the dynamic semantic intuitionistic fuzzy information. Specifically, we obtained the rankings of alternative products and their features to support consumers' purchase decisions using the intuitionistic fuzzy scoring function and the 'vertical projection distance' method. Finally, comparisons and experiments are provided to demonstrate the plausibility of our methods

Review of Feature Selection and Optimization Strategies in Opinion Mining

Opinion mining and sentiment analysis methods has become a prerogative models in terms of gaining insights from the huge volume of data that is being generated from vivid sources. There are vivid range of data that is being generated from varied sources. If such veracity and variety of data can be explored in terms of evaluating the opinion mining process, it could help the target groups in getting the public pulse which could support them in taking informed decisions. Though the process of opinion mining and sentiment analysis has been one of the hot topics focused upon by the researchers, the process has not been completely revolutionary. In this study the focus has been upon reviewing varied range of models and solutions that are proposed for sentiment analysis and opinion mining. From the vivid range of inputs that are gathered and the detailed study that is carried out, it is evident that the current models are still in complex terms of evaluation and result fetching, due to constraints like comprehensive knowledge and natural language limitation factors. As a futuristic model in the domain, the process of adapting scope of evolutionary computational methods and adapting hybridization of such methods for feature extraction as an idea is tossed in this paper

Review of Feature Selection and Optimization Strategies in Opinion Mining

Opinion mining and sentiment analysis methods has become a prerogative models in terms of gaining insights from the huge volume of data that is being generated from vivid sources. There are vivid range of data that is being generated from varied sources. If such veracity and variety of data can be explored in terms of evaluating the opinion mining process, it could help the target groups in getting the public pulse which could support them in taking informed decisions. Though the process of opinion mining and sentiment analysis has been one of the hot topics focused upon by the researchers, the process has not been completely revolutionary. In this study the focus has been upon reviewing varied range of models and solutions that are proposed for sentiment analysis and opinion mining. From the vivid range of inputs that are gathered and the detailed study that is carried out, it is evident that the current models are still in complex terms of evaluation and result fetching, due to constraints like comprehensive knowledge and natural language limitation factors. As a futuristic model in the domain, the process of adapting scope of evolutionary computational methods and adapting hybridization of such methods for feature extraction as an idea is tossed in this paper

Review of Feature Selection and Optimization Strategies in Opinion Mining

Opinion mining and sentiment analysis methods has become a prerogative models in terms of gaining insights from the huge volume of data that is being generated from vivid sources. There are vivid range of data that is being generated from varied sources. If such veracity and variety of data can be explored in terms of evaluating the opinion mining process, it could help the target groups in getting the public pulse which could support them in taking informed decisions. Though the process of opinion mining and sentiment analysis has been one of the hot topics focused upon by the researchers, the process has not been completely revolutionary. In this study the focus has been upon reviewing varied range of models and solutions that are proposed for sentiment analysis and opinion mining. From the vivid range of inputs that are gathered and the detailed study that is carried out, it is evident that the current models are still in complex terms of evaluation and result fetching, due to constraints like comprehensive knowledge and natural language limitation factors. As a futuristic model in the domain, the process of adapting scope of evolutionary computational methods and adapting hybridization of such methods for feature extraction as an idea is tossed in this paper