Kaiquan Xu

Mining comparative opinions from customer reviews for Competitive Intelligence

Competitive Intelligence is one of the key factors for enterprise risk management and decision support. However, the functions of Competitive Intelligence are often greatly restricted by the lack of sufficient information sources about the competitors. With the emergence of Web 2.0, the large numbers of customer-generated product reviews often contain information about competitors and have become a new source of mining Competitive Intelligence. In this study, we proposed a novel graphical model to extract and visualize comparative relations between products from customer reviews, with the interdependencies among relations taken into consideration, to help enterprises discover potential risks and further design new products and marketing strategies. Our experiments on a corpus of Amazon customer reviews show that our proposed method can extract comparative relations more accurately than the benchmark methods. Furthermore, this study opens a door to analyzing the rich consumer-generated data for enterprise risk management.

Sentiment classification: The contribution of ensemble learning

With the rapid development of information technologies, user-generated contents can be conveniently posted online. While individuals, businesses, and governments are interested in evaluating the sentiments behind this content, there are no consistent conclusions on which sentiment classification technologies are best. Recent studies suggest that ensemble learning methods may have potential applicability in sentiment classification. In this study, we conduct a comparative assessment of the performance of three popular ensemble methods (Bagging, Boosting, and Random Subspace) based on five base learners (Naive Bayes, Maximum Entropy, Decision Tree, K Nearest Neighbor, and Support Vector Machine) for sentiment classification. Moreover, ten public sentiment analysis datasets were investigated to verify the effectiveness of ensemble learning for sentiment analysis. Based on a total of 1200 comparative group experiments, empirical results reveal that ensemble methods substantially improve the performance of individual base learners for sentiment classification. Among the three ensemble methods, Random Subspace has the better comparative results, although it was seldom discussed in the literature. These results illustrate that ensemble learning methods can be used as a viable method for sentiment classification.

Two credit scoring models based on dual strategy ensemble trees

Decision tree (DT) is one of the most popular classification algorithms in data mining and machine learning. However, the performance of DT based credit scoring model is often relatively poorer than other techniques. This is mainly due to two reasons: DT is easily affected by (1) the noise data and (2) the redundant attributes of data under the circumstance of credit scoring. In this study, we propose two dual strategy ensemble trees: RS-Bagging DT and Bagging-RS DT, which are based on two ensemble strategies: bagging and random subspace, to reduce the influences of the noise data and the redundant attributes of data and to get the relatively higher classification accuracy. Two real world credit datasets are selected to demonstrate the effectiveness and feasibility of proposed methods. Experimental results reveal that single DT gets the lowest average accuracy among five single classifiers, i.e., Logistic Regression Analysis (LRA), Linear Discriminant Analysis (LDA), Multi-layer Perceptron (MLP) and Radial Basis Function Network (RBFN). Moreover, RS-Bagging DT and Bagging-RS DT get the better results than five single classifiers and four popular ensemble classifiers, i.e., Bagging DT, Random Subspace DT, Random Forest and Rotation Forest. The results show that RS-Bagging DT and Bagging-RS DT can be used as alternative techniques for credit scoring.

Text mining and probabilistic language modeling for online review spam detection

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Classifying Consumer Comparison Opinions to Uncover Product Strengths and Weaknesses

With the Web 2.0 paradigm, a huge volume of Web content is generated by users at online forums, wikis, blogs, and social networks, among others. These user-contributed contents include numerous user opinions regarding products, services, or political issues. Among these user opinions, certain comparison opinions exist, reflecting customer preferences. Mining comparison opinions is useful as these types of viewpoints can bring more business values than other types of opinion data. Manufacturers can better understand relative product strengths or weaknesses, and accordingly develop better products to meet consumer requirements. Meanwhile, consumers can make purchasing decisions that are more informed by comparing the various features of similar products. In this paper, a novel Support Vector Machine-based method is proposed to automatically identify comparison opinions, extract comparison relations, and display results with the comparison relation maps by mining the volume of consumer opinions posted on the Web. The proposed method is empirically evaluated based on consumer opinions crawled from the Web. The initial experimental results show that the performance of the proposed method is promising and this research opens the door to utilizing these comparison opinions for business intelligence.

Toward a Language Modeling Approach for Consumer Review Spam Detection

Numerous reports have indicated the severity of fake reviews (i.e., spam) posted to various e-Commerce or opinion sharing Web sites. Nevertheless, very few studies have been conducted to examine the trustworthiness of online consumer reviews because of the lack of an effective computational methodology. Unlike other kinds of Web spam, untruthful reviews could just look like other legitimate reviews (i.e., ham), and so it is difficult to apply any features to distinguish the two classes. One main contribution of our research work is the development of a novel computational methodology to combat online review spam. Our experimental results confirm that the KL divergence and the probabilistic language modeling based computational model is effective for the detection of untruthful reviews. Empowered by the proposed computational methods, our empirical study found that around 2% of the consumer reviews posted to a large e-Commerce site is spam.

Identifying valuable customers on social networking sites for profit maximization

With the tremendous popularity of social networking sites (SNS) in this era of Web 2.0, enterprises have begun to explore the feasibility of using SNS as platforms to conduct targeted marking and reputation management. Given huge number of users on SNS, how to choose appropriate users as the targets is the key for enterprises to conduct cost-effective targeted marketing and reputation management on SNS. This paper introduces a novel model for effectively identifying the most valuable users from SNS. Furthermore, we propose to use an optimization technique named semidefinite programming (SDP) to identify the most valuable customers that can generate the maximum of total profit. Our empirical evaluation on a real data set extracted from a popular SNS shows that the proposed model achieves much higher profits than benchmark methods. This study opens doors to more effective targeted marketing and reputation management on SNS.

Constructing intelligent and open mobile commerce using a semantic web approach

To achieve the full potential of mobile commerce (m-Commerce), many problems need to be resolved, such as how to make m-Commerce seamlessly span wide areas with heterogeneous information sources; how to improve the matchmaking between user requirements and product specifications; and how to make m-Commerce systems more intelligent in taking different actions according to different user context environments. We believe that the semantic web is a promising technology to solve these problems. In this paper, a framework is proposed in which semantic web ontology is used to model the contexts, user profiles, and product/service information. The semantic web service ontology, OWL-S, is extended for matching user requirements with product specifications at the semantic level, with context information taken into account. Semantic Web Rule Language (SWRL) is used for inferencing with context and user profile descriptions. Our system adopts a multi-agent infrastructure to deal with the integration and interaction between information sources and users. This paper demonstrates some of the great potential of semantic web technology for m-Commerce.

Sentiment community detection in social networks

With the increasing popularity of social networking sites and Web 2.0, people are building social relationships and expressing their opinions in the cyberspace. In this study, we introduce several novel methods to identify online communities with similar sentiments in online social networks. Our preliminary experiment on a real-world dataset demonstrates that our proposed method can detect interesting sentiment communities in social networks.

Battle of the Channels: The Impact of Tablets on Digital Commerce

The introduction of tablets in online retailing has created an additional touchpoint through which e-commerce firms can interact with consumers. In this paper, we seek to understand and measure the causal impact of tablets on e-commerce sales. In doing so, we examine the complementary and substitution impact of the tablet channel on the smartphone and PC channels. We rely on a unique data set from Alibaba, the largest e-commerce firm in the world, and exploit a natural experiment via the iPad app introduction to empirically identify our results. The results show that users’ adoption of tablets enhanced the overall growth of Alibaba’s e-commerce market, with an annual increase of approximately US