TY - GEN
T1 - Nanotechnology Performance Analysis Using Topic Modeling and Social Network Analysis
T2 - 22nd ACIS International Fall Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2021
AU - Sung, Wookjoon
AU - Kim, Changil
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In this study, in order to analyze nanotechnology trends, topic modeling analysis and text network analysis were conducted by collecting nanotechnology summary information that was developed between 2014 and 2019 and registered in NTIS. First, key keywords were selected through topic modeling analysis, and then a text network was built based on these keywords. As a result of classifying topics through topic modeling analysis, topics were classified into five topics based on the five major nanotechnology fields classified by the government. In addition, as a result of conducting a network analysis to examine the factors that affect nanotechnology performance, carbon, battery, metal, electrode, fiber, substrate, and graphene were found as factors that mainly influence nanotechnology. Next, as a result of analyzing the nanotechnology field according to the time period, the five major fields were from 2014 to 2015 (time 1), 2016 to 2017 (time 2), and 2018 to 2019 (time 3). The results of technology development have occurred evenly. However, it was confirmed that the degree of technology development by field differs depending on the period. As for the text network analysis, the most active development was in the nano-energy and environment field in the period 1, the nanomaterials and the nanodevices in the period 2, and the nanomaterials in the period 3.
AB - In this study, in order to analyze nanotechnology trends, topic modeling analysis and text network analysis were conducted by collecting nanotechnology summary information that was developed between 2014 and 2019 and registered in NTIS. First, key keywords were selected through topic modeling analysis, and then a text network was built based on these keywords. As a result of classifying topics through topic modeling analysis, topics were classified into five topics based on the five major nanotechnology fields classified by the government. In addition, as a result of conducting a network analysis to examine the factors that affect nanotechnology performance, carbon, battery, metal, electrode, fiber, substrate, and graphene were found as factors that mainly influence nanotechnology. Next, as a result of analyzing the nanotechnology field according to the time period, the five major fields were from 2014 to 2015 (time 1), 2016 to 2017 (time 2), and 2018 to 2019 (time 3). The results of technology development have occurred evenly. However, it was confirmed that the degree of technology development by field differs depending on the period. As for the text network analysis, the most active development was in the nano-energy and environment field in the period 1, the nanomaterials and the nanodevices in the period 2, and the nanomaterials in the period 3.
KW - Nanotechnology
KW - Technology performance structure
KW - Technology trend
KW - Text network analysis
KW - Topic modeling
UR - https://www.scopus.com/pages/publications/85123307593
U2 - 10.1007/978-3-030-92317-4_9
DO - 10.1007/978-3-030-92317-4_9
M3 - Conference contribution
AN - SCOPUS:85123307593
SN - 9783030923167
T3 - Studies in Computational Intelligence
SP - 114
EP - 127
BT - Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
A2 - Lee, Roger
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 24 November 2021 through 26 November 2021
ER -