## 5 Discussion  
Based on newly coined words and emoticon extraction process presented in this paper, we have constructed a newly coined word and emoticon extraction and classification system and an emotional analysis system with newly coined words and emoticon based on the emotional analysis process in Section 4. The system is built on JAVA JDK 1.6 and CentOS 6.5. MYSQL 5.5 is used as the database to store social data. The social data are extracted only from Twitter. We used twitter4j library and the Java Wrapper of Twitter API, in order to randomly collect tweets in Korean. The collection period is from 7 March 2018 to 7 May 2018, and a total of 4,210,744 tweets were collected. The collected tweets were processed through a preprocessing process to remove the URL, user ID, and hashtag. We collected 90,373 Naver Open dictionaries using the crawlers developed in the JAVA language for extracting new words from the collected tweets. In the collected tweets, if the word in the open dictionary is included, it is judged as a newly coined word, and 55,996 cases were extracted. Also, 31,340 character-type emoticons and 1171 4-bit type character-type emoticons were extracted for the emoticons. Image-type emoticons are excluded because they are not provided by Twitter. On Twitter, we collected 725,928 tweets from 6 August 2018 to 7 September 2018, with the keyword 'idol.' As a criterion for measuring the accuracy of the emotion analysis method presented in this paper, a person directly classified tweets having a positive inclination and a tweet having a negative inclination in a tweet. The process of categorizing 725,928 tweets by hand requires a lot of time and labor, limiting the number of positive tweets to 926, negative tweets to 327, with a total of 1253 validation tweets. The evaluation items of this experiment were emotion analysis accuracy using existing Korean dictionary-based emotion dictionary, emotion analysis accuracy using new word and emoticon emotion dictionary, emotion analysis accuracy using existing Korean dictionary-based emotion dictionary, and newly coined word and emoticon emotion dictionary, respectively. The tweets are pre-verified and the URL, user ID, and hashtag of tweets are removed. The emotion dictionary based on the existing Korean dictionary uses Korean sentiment analysis corpus (KOSAC). Korean Sentiment Analysis Corpus (KOSAC) consists of morphemes. Therefore, we extract morphemes from the tweets for verification in emotional analysis using emotional dictionary based on existing Korean dictionary, and then extract emotional words by matching Korean sentiment analysis corpus (KOSAC). [PERSON]'s Comorian morpheme analyzer 2.4 [10] was used for morphological extraction from the verification tweet. The measurement method verifies the correspondence between the tweet of positive inclination and the tweet of negative inclination classified by the person in the verification tweet. Figure 13 shows the frequency of use of newly coined words and emoticons in tweets used in experiments. In total, 65.4% of the collected tweets had newly coined words and emoticons used in the experiment. SNS users usually use standard words, newly coined words, and emoticons in the tweets. Figure 13: Frequency of newly coined words and emoticons used. Figure 14 shows the results of the analysis on the positive tweets. The accuracy of emotional analysis by Korea standard word with emotional dictionaries, the accuracy of emotional analysis by the proposed newly coined words and emoticon dictionary, the accuracy of emotional analysis by Korea standard word with emotional dictionaries and the proposed newly coined words and emoticon dictionary are compared. The accuracy of emotional analysis by Korea standard word with emotional dictionaries is 53.5%. The accuracy of emotional analysis by the proposed newly coined words and emoticon dictionary is 56.3%. The accuracy of emotional analysis by Korea standard word with emotional dictionaries and the proposed newly coined words and emoticon dictionary is 80.2%.