Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology

社会化媒体 数据科学 新兴技术 未来研究 政府(语言学) 光伏系统 计算机科学 业务 工程类 万维网 语言学 电气工程 哲学 人工智能
作者
Xin Li,Qianqian Xie,Jiaojiao Jiang,Yuan Zhou,Lucheng Huang
出处
期刊:Technological Forecasting and Social Change [Elsevier]
卷期号:146: 687-705 被引量:115
标识
DOI:10.1016/j.techfore.2018.06.004
摘要

Monitoring the emergence of emerging technologies helps managers and decision makers to identify development trends in emerging technologies is crucial for government research and development (R&D), strategic planning, social investment, and enterprise practices. Researchers usually use academic papers and patent data to identify and monitoring the trends of emerging technologies from a technological perspective, but they rarely make use of social media data (e.g., such as Twitter data) related to emerging technologies. Analysis of this social media data is of great significance to understand the emergence of emerging technologies and gain insight into development trends. Therefore, this paper proposes a framework that uses patent analysis and Twitter data mining to monitoring the emergence of emerging technologies and identify changing trends of these emerging technologies. The perovskite solar cell technology is selected as a case study. In this case, we used patent analysis to monitoring the evolutionary path of perovskite solar cell technology. We applied Twitter data mining to analyze Twitter users' sense of, response to, and expectations for this perovskite solar cell technology. We also identified the professional types of Twitter users and examined changes in their topics of interest over time to track the emergence of perovskite solar cell technology. We analyzed a comparison of the results of patent analysis and Twitter data mining to identify development trends of perovskite solar cell technology. This paper contributes to our understanding of how technologies emerge and develop, as well as the technology forecasting and foresight methodology, and will be of interest to solar photovoltaic technology R&D experts.
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