Despite hospitality and tourism researchers’ recent attempts on examining different aspects of online word-of-mouth [WOM], its impact on hotel sales remains largely unknown in the existing literature. To fill this void, we conduct a study to empirically investigate the impact of online consumer-generated reviews on hotel room sales. Utilizing data collected from the largest travel website in China, we develop a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings. Our results indicate a significant relationship between online consumer reviews and business performance of hotels.

The impact of online user reviews on hotel room sales [Summary]

The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided.

Review: Intrusion detection by machine learning: A review

http://levis.sggw.pl/~rew/scenes/pdf/Ozesmi.pdf

Ecological models based on people’s knowledge: a multi-step fuzzy cognitive mapping approach

Many researches have argued that Artificial Neural Networks (ANNs) can improve the performance of intrusion detection systems (IDS) when compared with traditional methods. However for ANN-based IDS, detection precision, especially for low-frequent attacks, and detection stability are still needed to be enhanced. In this paper, we propose a new approach, called FC-ANN, based on ANN and fuzzy clustering, to solve the problem and help IDS achieve higher detection rate, less false positive rate and stronger stability. The general procedure of FC-ANN is as follows: firstly fuzzy clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to formulate different base models. Finally, a meta-learner, fuzzy aggregation module, is employed to aggregate these results. Experimental results on the KDD CUP 1999 dataset show that our proposed new approach, FC-ANN, outperforms BPNN and other well-known methods such as decision tree, the naive Bayes in terms of detection precision and detection stability.

A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering

Application of SVM and ANN for intrusion detection

This paper extracted discrete emotions from online reviews based on an emotion classification approach, and examined the differential effects of three discrete emotions (anger, fear, sadness) on perceived review helpfulness. We empirically tested the hypotheses by analyzing the “verified purchase” reviews on Amazon.com. The findings of this study extend the previous research by suggesting that product type moderates the effects of emotions on perceived review helpfulness. Anger embedded in a customer review exerts a greater negative impact on perceived review helpfulness for experience goods than for search goods. Fear embedded in a review is identified as an important emotional cue to positively affect the perceived review helpfulness with more persuasive messages. As the level of sadness embedded in a review increases, perceived review helpfulness decreases. These findings contribute to a better understanding of the important role of emotions embedded in reviews on the perceived review helpfulness. This study also provides practical insights related to the presentation of online reviews and gives suggestions for consumers regarding how to select and write a helpful review.

Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews

This paper investigates the asymmetric costs of false positive and negative errors to enhance the IDS performance. The proposed method utilizes the neural network model to consider the cost ratio of false negative errors to false positive errors. Compared with false positive errors, false negative errors incur a greater loss to organizations which are connected to the systems by networks. This method is designed to accomplish both security and system performance objectives. The results of our empirical experiment show that the neural network model provides high accuracy in intrusion detection. In addition, the simulation results show that the effectiveness of intrusion detection can be enhanced by considering the asymmetric costs of false negative and false positive errors.

The neural network models for IDS based on the asymmetric costs of false negative errors and false positive errors

In this paper, we investigate ways to apply news information on the Internet to the prediction of interest rates. We developed the Knowledge-Based News Miner (KBNMiner), which is designed to represent the knowledge of interest rate experts with cognitive maps (CMs), to search and retrieve news information on the Internet according to prior knowledge, and to apply the information, which is retrieved from news information, to a neural network model for the prediction of interest rates.

This paper focuses on improving the performance of data mining by using prior knowledge. Real-world interest rate prediction data is used to illustrate the performance of the KBNMiner. Our integrated approach, which utilizes CMs and neural networks, has been shown to be effective in experiments. While the 10-fold cross validation is used to test our research model, the experimental results of the paired t-test have been found to be statistically significant.

Knowledge-based data mining of news information on the Internet using cognitive maps and neural networks

This study has proposed an approach that enables online stores to offer customized marketing by segmenting their customers based on customers' psychographic data Online stores can concentrate on more profitable activities by identifying customers' value as they segment their customers into a few groups of customers with similar intentions to purchase To segment online customers, based on previous research that explains the behavior of online customers regarding purchasing, the approach has employed the factors that affect the customers' intention to purchase on the Web We integrated the clustering results of SOM (self-organized map) and the k-means algorithm into a single model Online stores can develop promotional marketing and offer personalized service for e-customers, who are more valuable and more promising, according to the market segments presented by our approach

Segmenting customers in online stores based on factors that affect the customer's intention to purchase

With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com. By comparing the relative importance of each topic, we conclude that many tourists prefer to provide “suggestion” reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction “management” obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains.

https://www.mdpi.com/2071-1050/9/10/1765/pdf

Taeho， Hong

Papers

Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews

The neural network models for IDS based on the asymmetric costs of false negative errors and false positive errors

Knowledge-based data mining of news information on the Internet using cognitive maps and neural networks

Segmenting customers in online stores based on factors that affect the customer's intention to purchase

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach