Powerful methods for interactive exploration and search from collections of free-form textual documents are needed to manage the ever-increasing flood of digital information. In this article we present a method, WEBSOM, for automatic organization of full-text document collections using the self-organizing map (SOM) algorithm. The document collection is ordered onto a map in an unsupervised manner utilizing statistical information of short word contexts. The resulting ordered map where similar documents lie near each other thus presents a general view of the document space. With the aid of a suitable (WWW-based) interface, documents in interesting areas of the map can be browsed. The browsing can also be interactively extended to related topics, which appear in nearby areas on the map. Along with the method we present a case study of its use.

Self-organizing maps of document collections: A new approach to interactive exploration

As the largest organ in human body, skin acts as a physicochemical barrier, offering protection against harmful environmental stressors, such as chemicals, pathogens, temperature and radiation. Non...

Cyanobacteria and microalgae bioactive compounds in skin-ageing: potential to restore extracellular matrix filling and overcome hyperpigmentation.

A decade of systematic literature review on Airbnb: the sharing economy from a multiple stakeholder perspective.

Service-quality is a major determinant of tourists’ choice of hotel. Tourists are likely to refer to user-created online reviews on online forums and other social networks comprising critical text data resources that represent the service quality experienced. This study develops a decision-support framework for hotel managers to comprehensively estimate the degree of guest satisfaction (i.e., service-quality measure) together with benchmarking guidelines on service quality improvement. The decision-support framework facilitates the discovery of the most important service attributes from the online reviews of 52 five-star hotels in South Korea (data preprocessing component). It clusters the reviews according to the discovered service attributes and conducts sentiment analysis to estimate the magnitude of positive opinions (sentiment analysis component). Further, it applies an output-oriented data envelopment analysis to calculate the degree of guest satisfaction and service positioning for each hotel (benchmarking analysis component). The framework also investigates how the service quality of each hotel is associated with that of others in terms of each service attribute (quality association analysis component). The framework enables managers to comprehensively understand the achievement goals of service quality for the service attributes by means of online review data analytics and modeling.

An opinion-driven decision-support framework for benchmarking hotel service

Where the host is part of the deal: Social and economic value in the platform economy

This study analyzes online customer reviews in order to investigate customers' preferences regarding cosmetic products. Based on the marketing firm theory, this research explores the possibility of enhancing the bilateral contingent relationships between the customer and the marketing firm within the cosmetics domain. Hence, this study applies market‐search concepts by extracting customer reviews and employing text analytics to identify reinforcers and factors in cosmetic products, which customers are expecting, and their sentiments towards them. Our results suggest that some reinforcers are shared among all customers, but some vary among the different customer segments based on their age and skin tone.

Exploring customer online reviews for new product development: The case of identifying reinforcers in the cosmetic industry

On the relative impact of male and female sellers’ profile image and its facial expressions upon peer users’ behavior on AirbnbTM

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Exploring customer online reviews for new product development: The case of identifying reinforcers in the cosmetic industry

On the relative impact of male and female sellers’ profile image and its facial expressions upon peer users’ behavior on AirbnbTM