Nowadays, based on mobile devices and internet, social network services (SNS) are common trends to everyone. Social opinions as public opinions are very important to the government, company, and a person. Analysis and decision of social polarity of SNS about social happenings, political issues and government policies, or commercial products is very critical to the government, company, and a person. Newly coined words and emoticons on SNS are created every day. Specifically, emoticons are made and sold by a person or companies. Newly coined words are mostly made and used by various kinds of communities. The SNS big data mainly consist of normal text with newly coined words and emoticons so that newly coined words and emoticons analysis is very important to understand the social and public opinions. Social big data is informally made and unstructured, and on social network services, many kinds of newly coined words and various emoticons are made anonymously and unintentionally by people and companies. In the analysis of social data, newly coined words and emoticons limit the guarantee the accuracy of analysis. The newly coined words implicitly contain the social opinions and trends of people. The emotional states of people significantly are expressed by emoticons. Although the newly coined words and emoticons are an important part of the social opinion analysis, they are excluded from the emotional dictionary and social big data analysis. In this research, newly coined words and emoticons are extracted from the raw Twitter’s twit messages and analyzed and included in a pre-built dictionary with the polarity and weight of the newly coined words and emoticons. The polarity and weight are calculated for emotional classification. The proposed emotional classification algorithm calculates the weight of polarity (positive or negative) and results in total polarity weight of social opinion. If the total polarity weight of social opinion is more than the pre-fixed threshold value, the twit message is decided as positive. If it is less than the pre-fixed threshold value, the twit message is decided as negative and the other values mean neutral opinion. The accuracy of the social big data analysis result is improved by quantifying and analyzing emoticons and newly coined words. Full article

Self- and mutual-help by citizens are important as well as social-help from the local governments, for disaster prevention and mitigation. Then, town watching and disaster prevention map-making workshops are held to review the town and promote self- and mutual-help by citizens. On the other hand, the use of social media for information sharing during and after disasters has been gaining attention. To facilitate information sharing in disasters, we developed a web system, Disaster Information Tweeting and Mapping System (DITS/DIMS). From the above background, we organized a town-watching workshop using DITS/DIMS in October 2018 in Minami Ward, Sapporo City, Hokkaido, Japan; affected area of the Hokkaido Eastern Iburi Earthquake in September 2018. In this paper, we explain the workshop procedure, outcome, questionnaire survey results, and post-meeting. The questionnaire survey result shows that the workshop educated the participants about posting useful information on social media during a disaster. In addition, at the post-meeting, the participants recognized that they had reviewed the town only from the perspective of “daily life” convenience before the earthquake, and they had not evaluated the “emergency viewpoint.” Therefore, the workshop was a meaningful opportunity for the participants to review the town in terms of disaster prevention and mitigation. Full article