# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 3 Analyzing the AI Hub Corpus Using LIWC
### Korean-English Parallel Corpus
Unlike other corpora in which impersonal pronouns are predominant, this corpus includes both colloquial and dialogic sentences because it comprises interactive conversations between the first-person perspective and other person perspectives. Syntactic analysis is defined as an analytic approach that informs us on the grammatical meaning of specific sentences or parts of such sentences. This approach avails the type of tone and atmosphere used in sentences through the summary language variables category. We also obtain the sentence length from 'word count (wc)', 'word per sentence (wps)', and lengthy word count using 'Sixltr'. We explore the sentences based on whether they are represented using statements, questions, or quotations using the punctuation category. We use the time orientations category to understand the point of view, and we investigate the 'assents (assent)', 'fillers (filler)','swear words (swear)' in the informal language markers category. In the end, these features contribute to understanding the syntactic information of the corpus. We show the 'thinking (analytic)', 'clout (clout)' (i.e., the representation of trust), 'authenticity (authentic)' (i.e., the representation of sincerity), and 'emotional tone (tone)' as 94.16%, 65.39%, 27.76%, and 54.48%, respectively. We can find that the prevalence of 'analytic' is relatively low whereas that of 'clout', 'authentic', and 'tone' is relatively high. This reflects the characteristics of each component in the corpus that contains various descriptive styles (i.e., colloquial or literary), rather than focusing on conveying and explaining specific domain knowledge. Various descriptive styles of this corpus can also be found by inspecting punctuation category which shows relatively high appearance rate of 'question marks (QMark)'. In the results of the linguistic dimensions category, we establish that the average word count of a sentence is 25.39, and 'Sixltr' accounts for 25% of the total word count. The articles and prep categories also account for a large proportion at 10.4% and 15.5%, respectively, and as a result of the analysis of the time orientations category, the ratios are high in the order of present focus, past focus, and future focus. Additionally, in the grammar category, words that directly represent 'number (number)' are used approximately twice as often as 'quantifiers (quant)' representing quantities. In the informal language mark category, the values are higher than those of the other categories. It is noteworthy that'swear words', such as 'damn' and'shit', and 'fillers', such as 'you know' and 'i mean', which are used as interludes between conversations, are close to zero in other corpora results. It seems that this is because the corpus contains written, colloquial, and dialogue language, unlike general data, which consists only of written or spoken language. In semantic analysis, semantics is the study of analyzing meaning in units of text, sentences, and phrases. In this study, we attempt to understand the corpus in depth by checking which of the various topics, such as drives and biological process, has a high ratio. In the corpus, the relativity category corresponding to a relatively trivial topic was found to be at the highest level at 14.29%, and'space (space)' accounted for the highest prevalence at 7.83%. Next are the drives, cognitive process, personal concerns, and social process categories in that order. Specifically, in the personal concerns category, 'work (work)' occupied more than half. This result is because, unlike other corpora, this corpus includes colloquial words and dialogues. For this reason, the relatively trivial topic, which is the topic of conversation, and the individual's sense of purpose, thoughts, and interests are relatively clearly revealed. Sentiment analysis involves analyzing the degree of positivity and negativity appearing in text. LIWC supports the analysis of the scale of 'positive emotion (posemo)' and 'negative emotion (negemo), such as 'anger', 'anxiety', and'sadness.'