Online Information-Based Product Evolution Course Mining and Prediction

In today’s fiercely competitive business environment, product design and development play critical roles in an enterprise’s success; therefore, consumer demand must be understood. Enterprises used to understand their consumers’ demands through time-consuming questionnaire surveys and statistical analyses. As the Internet and the popularity of virtual communities have grown, more consumers are leaving comments about their perceptions of the appeal of products on online social media platforms, thus enabling enterprises to more objectively understand consumers’ product preferences and demands. Therefore, determining how to effectively assist enterprises in analyzing valuable information beneficial to product design and development that can be gleaned from the large amount of social media data available is critical to promoting an enterprise’s competitive advantage in the product market. However, previous studies have primarily focused on understanding consumer viewpoints of products through review articles or electronic word-of-mouth (eWOM; it is a common channel for spreading product appraisals) from online social media. For these review articles and eWOM that can imply consumer demand for product features, there were no relevant studies focused on analyzing consumer demand by using both online review articles and eWOM for product feature evolution. Therefore, for new product features, this study developed a mechanism for product evolution course mining from product-related review articles (such as product functions or specifications) and eWOM on online social media to realize the prediction of future product features or specifications, and then assist enterprises in rapidly and accurately grasping product development trends to effectively discern key reference information for product design and development. The study was achieved by (1) designing a process for online information-based product evolution course mining and prediction, (ii) developing techniques related to online information-based product evolution course mining and prediction, and (iii) implementing and evaluating an online information-based product evolution course mining and prediction mechanism. The experimental results indicated that approximately 81% of developers were satisfied with the proposed system for predicting smartphone features. The results of the study can facilitate the mining and prediction of online information-based product evolution course to assist enterprises in rapidly and accurately grasping product development trends and effectively provides important reference information for product design and development; therefore, it can enhance competitive advantages and customer satisfaction of enterprise products in the marketplace.