@article{ART002877410},
author={Youngmin Yun},
title={Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns},
journal={The Japanese Language Association of Korea},
issn={1229-7275},
year={2022},
number={73},
pages={73-92},
doi={10.14817/jlak.2022.73.73}
TY - JOUR
AU - Youngmin Yun
TI - Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns
JO - The Japanese Language Association of Korea
PY - 2022
VL - null
IS - 73
PB - The Japanese Language Association Of Korea
SP - 73
EP - 92
SN - 1229-7275
AB - This paper introduces one methodology of Japanese research based on data and aims to explore the co-occurrence relationship of Japanese emotion vocabulary using “network analysis”. The subject of the analysis is “100 books of Shincho Bunko”. Specifically, it collates texts of 65 Japanese novels in “100 of Shincho Bunko”, which completed the morphological comprehensive analysis, to “Japanese sentiment expression dictionary (JIWC-Dictionary)” which was used as a criterion for categorizing emotions. Targeting two-character kanji noun vocabulary, it then uses the network analysis method and visualizes the co-occurrence relationship of the various vocabulary that belong to the emotion categories of “sadness”, “anxiety”, “anger”, “disgust”, “trust”, “surprise”, and “excited”. The two-character kanji noun vocabulary to be analyzed was 33,207 out of 134,370 total tokens, of which 211 were output as “TRUE” from the seven emotion categories. “Anxiety” shows the highest number with 45, followed by “trust” 36, “disgust” 30, “sadness” 26, “surprise” and “excited” each 25, and 24 from “anger”. With these results, we observed the aspect of the vocabulary by emotion category, and at the same time, listed the co-occurrence relationship for the “earthquake” of “anxiety”, which contained the most vocabulary in the emotion category.
KW - Data analysis;Text mining;Co-occurrence;Networking;Emotional vocabulary
DO - 10.14817/jlak.2022.73.73
ER -
Youngmin Yun. (2022). Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns. The Japanese Language Association of Korea, 73, 73-92.
Youngmin Yun. 2022, "Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns", The Japanese Language Association of Korea, no.73, pp.73-92. Available from: doi:10.14817/jlak.2022.73.73
Youngmin Yun "Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns" The Japanese Language Association of Korea 73 pp.73-92 (2022) : 73.
Youngmin Yun. Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns. 2022; 73 : 73-92. Available from: doi:10.14817/jlak.2022.73.73
Youngmin Yun. "Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns" The Japanese Language Association of Korea no.73(2022) : 73-92.doi: 10.14817/jlak.2022.73.73
Youngmin Yun. Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns. The Japanese Language Association of Korea, 73, 73-92. doi: 10.14817/jlak.2022.73.73
Youngmin Yun. Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns. The Japanese Language Association of Korea. 2022; 73 73-92. doi: 10.14817/jlak.2022.73.73
Youngmin Yun. Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns. 2022; 73 : 73-92. Available from: doi:10.14817/jlak.2022.73.73
Youngmin Yun. "Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns" The Japanese Language Association of Korea no.73(2022) : 73-92.doi: 10.14817/jlak.2022.73.73
