LAUSR

ABSTRACT The current product design not only takes into account the function and reliability, but also concerns about the affective aspects in order to meet the consumers’ emotional needs. However, there is always a gap between affective intentions of manufacturers and affective responses of consumers. Traditional methods rely on manual surveys to understand the gap, which is costly, time-consuming and in a small scale. In this paper, we propose a text mining method to extract affective intentions and affective responses from the online product description and consumer reviews. We build an affective profile for each product to visualise the gap between affective responses and affective intentions of the product. To evaluate the effectiveness of the proposed method, a case study is conducted based on the public data from Amazon.com. We construct affective profiles for selected products and analyze affective gaps. We also evaluate the usefulness of the extracted affective information in product recommendations. The results showed that the gap between consumer's affective responses and manufacturer's affective intentions can be identified and visualised, which may help manufacturers to improve their products and services. Affective information is also useful for product recommendations.