Machine translation (MT) system aims to translate source language into target\nlanguage. Recent studies on MT systems mainly focus on neural machine\ntranslation (NMT). One factor that significantly affects the performance of NMT\nis the availability of high-quality parallel corpora. However, high-quality\nparallel corpora concerning Korean are relatively scarce compared to those\nassociated with other high-resource languages, such as German or Italian. To\naddress this problem, AI Hub recently released seven types of parallel corpora\nfor Korean. In this study, we conduct an in-depth verification of the quality\nof corresponding parallel corpora through Linguistic Inquiry and Word Count\n(LIWC) and several relevant experiments. LIWC is a word-counting software\nprogram that can analyze corpora in multiple ways and extract linguistic\nfeatures as a dictionary base. To the best of our knowledge, this study is the\nfirst to use LIWC to analyze parallel corpora in the field of NMT. Our findings\nsuggest the direction of further research toward obtaining the improved quality\nparallel corpora through our correlation analysis in LIWC and NMT performance.\n