## 3 Sentiment Analysis Data Processing
### Sentiment Words Refinement  
In order to select the final sentiment words used for a two-dimensional distribution map among 68 sentiment words from the user survey, we collected and compared sentiment word data in existing movie reviews, eliminating words that were rarely used. This procedure consisted of three phases as shown below.  
Crawling: Movie review data used in this research were collected from movie information service NAVER [25], a web portal site with the largest number of users in Korea. We designed a web crawler to automate sentiment word collection from movie reviews. This crawler covered three stages: (1) collecting unrefined movie reviews and tags in the NAVER movie web page, (2) refining collected data suitable for this research, and (3) extracting sentiment words based on analysis of refined data. As a result, we obtained 4,107,605 reviews of 2289 movies from 2004 to 2015.  
Establishing sentiment word dictionary: We divided text data into morphemes collected through the crawling process using a mecab-ko-lucene-analyzer [26] and further extracted sentiment morphemes. A total of 133 morpheme clusters were selected through several text mining processes. A morpheme is the smallest grammatical unit in a language. In other words, it is the smallest meaningful unit of a language [27].  
Each emotion morpheme selected was classified according to 68 kinds of detailed sentiment word categories. A sentiment word dictionary classified by the chosen sentiment word was then established. Extracting emotion morphemes and classifying them by category were conducted in consultation with Korean linguists.  
To produce more accurate results, we eliminated less influential sentiment word clusters after matching them with actual movie review data. We calculated Term (_w_) Frequency (_tf_: Term Frequency) of each sentiment word cluster (_t_) suggested by the following formula.  
\[tf(t,d)=\Sigma\overset{j}{{}_{i=0}}f(w_{i},d) \tag{1}\]  
\[\textbf{{j}}=\textit{number of words in sentimental group {t}}\]  
_The number of times that term **t** occurs in document **d**_  
Then, inverse document frequency (_idf_) was also drawn from this formula to lower the weight of the general sentiment word group. The inverse document frequency (_idf_) is \"the logarithmically scaled inverse fraction of the documents that contain the word\" [28]. It reduces the number of terms with a high incidence across documents.  
\[idf(t,\textbf{{D}})=log\bigg{(}\frac{\textbf{{N}}}{|[d\in\textbf{{D}}:t\in d ]|}\bigg{)} \tag{2}\]  
\[\textbf{{N}}=\textit{total number of documents in the corpus {N}}=|\textbf{{D}}|\]  
\[\textbf{{D}}=\textit{document set}\]  
TF-IDF score of sentiment word clusters on each movie was calculated using the following formula:  
\[TFIDF(\textbf{{t}},\textbf{{d}},\textbf{{D}})=tf(\textbf{{t}},\textbf{{d}}) *idf(\textbf{{t}},\textbf{{D}}) \tag{3}\]  
We next considered the maximum TF-IDF score that might appear from each sentiment word in order to decrease the number of sentiment words. For example, the word \"Aghast\" showed a TF-IDF score of no more than 0.8% in every movie, whereas \"Sweet\" scored 42% for at least one movie. We eliminated sentiment words with TF-IDF scores under 10%. Eventually, we selected 36 sentiment words. These sentiment word clusters were broadly divided into \"Happy,\" \"Surprise,\" \"Boring,\" \"Sad,\" \"Anger,\" \"Disgust,\" and \"Fear\" as shown in Table 1.