Technology opportunity discovery by structuring user needs based on natural language processing and machine learning


Autoři: Taeyeoun Roh aff001;  Yujin Jeong aff001;  Hyejin Jang aff001;  Byungun Yoon aff001
Působiště autorů: Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, Seoul, South Korea aff001
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223404

Souhrn

Discovering technology opportunities from the opinion of users can promote successful technological development by satisfying the needs of users. However, although previous approaches using opinion mining only have classified various needs of users into positive or negative categories, they cannot derive the main reasons for their opinion. To solve this problem, this research proposes an approach to exploring technology opportunity by structuring user needs with a concept of opinion trigger of objects and functions of the technology-based products. To discover technology opportunity, first, an opinion trigger is identified from review data using Naïve Base classifier and natural language processing. Second, the opinion triggers and patent keywords that have a similar meaning in context are clustered to discover the needs of the user and need-related technology. Then, the sentimental values of needs are calculated through graph-based semi-supervised learning. Finally, the needs of the user are classified in resolving the problem of vacant technology to discover technology opportunity. Then, an R&D strategy of each opportunity is suggested based on opinion triggers, patent keywords, and their property. Based on the concept of opinion trigger-based methodology, a case study is conducted on automobile—related reviews, extracting the customer needs and presenting important R&D projects such as an extracted need (cargo transportation) and its R&D strategy (resolving contradiction). The proposed approach can analyze the needs of user at a functional level to discover new technology opportunities.

Klíčová slova:

Linguistic morphology – Natural language processing – Patents – Semantics – Transportation – Vector spaces – Text mining – Brakes


Zdroje

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Článek vyšel v časopise

PLOS One


2019 Číslo 10