Product packaging is a significant factor in a buyer's purchasing decision. We have developed a method for creating package images reflecting consumers' taste impressions that balances the need to provide product information and the need to motivate purchasing. It uses a database showing the correspondence between adjectives and colors as extracted from consumer reviews. This correspondence is used to revise the colors in the original package image. Evaluation was done by having 40 participants drink target beverages and answer questions before and after drinking regarding their impressions of the taste and their desire to drink the beverage. The results revealed that displaying appropriately revised images reduced the gap between the expected taste when viewing the image and the actual taste. Displaying appropriately revised images should motivate purchasing decisions as well as increase product satisfaction.

In this study we introduce AdjScales, a method for scaling similar adjectives by their strength. It combines existing Web-based computational linguistic techniques in order to automatically differentiate between similar adjectives that describe the same property by strength. Though this kind of information is rarely present in most of the lexical resources and dictionaries, it may be useful for language learners that try to distinguish between similar words. Additionally, learners might gain from a simple visualization of these differences using unidimensional scales. The method is evaluated by comparison with annotation on a subset of adjectives from WordNet by four native English speakers. It is also compared against two non-native speakers of English. The collected annotation is an interesting resource in its own right. This work is a first step toward automatic differentiation of meaning between similar words for language learners. AdjScales can be useful for lexical resource enhancement.

Our aim is to develop an intuitive and effective sound retrieval method for non-expert users. Such a retrieval method should be developed to accommodate a human's perceptual features. We therefore first conducted an experiment to clarify how people represent sound. A participant listens to one sound stimulus and then conveys the sound to a partner. The results indicated that people used mostly verbal description categorized in three groups: the sound itself, the sound's situation, and the sound's impression. Based on these results, we propose three types of keywords: onomatopoeia, sound source, and adjective, which are typical keywords of the above three groups of sound description, for sound retrieval. This retrieval method was implemented for a sound database. Our method can increase the varieties of sounds able to be retrieved and allow users to intuitively search sounds because users can retrieve sounds by using keywords that are most natural to them.