Application of a topic model visualisation tool to a second language
Maria Skeppstedt, Magnus Ahltorp, Andreas Kerren, Rafał Rzepka, Kenji Araki · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
We explored adaptions required for applying a topic modelling tool to a language that is very different from the one for which the tool was originally developed. The tool, which enables text analysis on the output of topic modelling, was developed for English, and we here applied it on Japanese texts. As white space is not used for indicating word boundaries in Japanese, the texts had to be pre-tokenised and white space inserted to indicate a token segmentation, before the texts could be imported into the tool. The tool was also extended by the addition of word translations and phonetic readings to support users who are second-language speakers of Japanese.