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In this study, we focus on ethical education as a means to improve artificial companion’s conceptualization of moral decision-making process in human users. In particular, we focus on automatically determining whether changes in ethical education influenced core moral values in humans throughout the century. We analyze ethics as taught in Japan before WWII and today to verify how much the pre-WWII moral attitudes have in common with those of contemporary Japanese, to what degree what is taught as ethics in school overlaps with the general population’s understanding of ethics, as well as to verify whether a major reform of the guidelines for teaching the school subject of “ethics” at school after 1946 has changed the way common people approach core moral questions (such as those concerning the sacredness of human life). We selected textbooks used in teaching ethics at school from between 1935 and 1937, and those used in junior high schools today (2019) and analyzed what emotional and moral associations such contents generated. The analysis was performed with an automatic moral and emotional reasoning agent and based on the largest available text corpus in Japanese as well as on the resources of a Japanese digital library. As a result, we found out that, despite changes in stereotypical view on Japan’s moral sentiments, especially due to historical events, past and contemporary Japanese share a similar moral evaluation of certain basic moral concepts, although there is a large discrepancy between how they perceive some actions to be beneficial to the society as a whole while at the same time being inconclusive when it comes to assessing the same action’s outcome on the individual performing them and in terms of emotional consequences. Some ethical categories, assessed positively before the war, while being associated with a nationalistic trend in education have also disappeared from the scope of interest of post- war society. The findings of this study support suggestions proposed by others that the development of personal AI systems requires supplementation with moral reasoning. Moreover, the paper builds upon this idea and further suggests that AI systems need to be aware of ethics not as a constant, but as a function with a correction on historical and cultural changes in moral reasoning.

In this paper we analyzed how much religious vocabulary, in particular Buddhist vocabulary taken from the largest online dictionary of Buddhist terms, is present in everyday social space of Japanese people, particularly, in Japanese blog entries appearing on a popular blog service (Ameba blogs). We interpreted the level of everyday usage of Buddhist terms as appearance of such terms in the consciousness of people. We further analyzed what emotional and moral associations such contents generate. In particular, we analyzed whether expressions containing Buddhist vocabulary are considered appropriate or not from a moral point of view, as well as the emotional response of Internet users to Buddhist terminology. As a result of analyzing the data, we found out that Buddhist terms were in fact not absent as a theme from Japanese blogs and generated a strong emotional response. However, while the general reaction to several expressions using Buddhist terms was as expected, there were sometimes surprising twists in terms of social consequences, major discrepancies between what is perceived as ethically correct behavior between the Buddhist doctrine and the reasoning of the general population, as well as a considerate number of terms which have lost their original meaning and instead became slang expressions.

The ways of how people communicate on the Internet have adapted to the limited communication channel. Since the most meaning-rich while also concise and compressible mean of communication is still represented by textual information, Internet communication has adapted to support it with other information required for natural communication, while retaining the consistency of the medium. One of the means widely used by many generations of Internet users are the emoticons, used to express information that cannot be fully transmitted only by text, such as emotions or feelings. However, not all emoticons developed and widely used everyday are easily understandable to all users. Emoticon usage proficiency differs among specific persons, as well as between more general user groups. Moreover, the meaning of emoticons themselves can also be ambiguous, especially, when used without other information. In this research, we aimed to determine a correspondence between emoticons and linguistic expressions corresponding to them, such as onomatopoeia, etc., to estimate the general level of meaning ambiguity of emoticons. In the research, we first performed a survey in which we asked users about which emoticon conveys which meaning. Then we applied the results of the survey to quantify the understandability, or meaning ambiguity, of the emoticons. Based on the quantification, we propsed a method for specifying the emoticon’s meaning ambiguity, in which we applied the aility of the emotion to be expressed in everyday vocabulary, or its linguistic expressibility. Three versions of the proposed method showed that it is possible to estimate emoticon meaning ambiguity, although future experiments and additional data collection is required to maximize the performance. The method could help users apply more meaningful and understandable emoticons in their everyday communication.

In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported the validity of the proposed approach by revealing a number of valuable information on the vocabulary used by suicidal users and helped to pin-point false predictors. For example, we were able to specify that death-related words, typically associated with suicidal posts in the majority of the literature, become false predictors, when they co-occur with apostrophes, even in high-risk subreddits. On the other hand, the category-pair based disambiguation helped to specify that death becomes a predictor only when co-occurring with future-focused language, informal language, discrepancy, or 1st person pronouns. The promising applicability of the approach was additionally analyzed for its limitations, where we found out that although LIWC is a useful and easily applicable tool, the lack of any contextual processing makes it unsuitable for application in psychological and linguistic studies. We conclude that disadvantages of LIWC can be easily overcome by creating a number of high-performance AI-based classifiers trained for annotation of similar categories as LIWC, which we plan to pursue in future work.