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Article Emotional Structure of Dialogues in Aging-Themed Movies: an Analysis on Text Mining(Sage Publications inc, 2024) Ergun, Duygu; Takan, Savas; Katipoglu, GokmenEmotions play an important role in the process of media messages transforming the audience. In our study, starting from the question of what might be the dominant emotions in old age-themed cinema texts, it is aimed to obtain clues about how old age is constructed in cinema through these emotions and what kind of emotional structure about old age is imposed on the audience. In this direction, emotion analysis was applied to the dialogues of 379 old age-themed films shot in a period of 100 years to determine what kind of emotional structure is constructed in all old age-themed cinema texts. As a result, it was found that the most dominant emotion was anger and disgust. The findings were categorized according to three different periods (1920-1969, 1970-1999, 2000-2020) and interpreted in a descriptive approach in a historical perspective and within the framework of the cinemas of the in addition to the EU, Germany, France, the UK, and Japan, Turkish cinema to make a comparison, where the most old age films were made. It is expected that our study will set an example for text mining research in cinema and offer an alternative perspective to the discussions on the phenomenon of aging in cinema.Review Citation - WoS: 1Citation - Scopus: 2Bias in human data: A feedback from social sciences(Wiley Periodicals, inc, 2023) Takan, Savas; Ergun, Duygu; Yaman, Sinem Getir; Kilincceker, OnurThe fairness of human-related software has become critical with its widespread use in our daily lives, where life-changing decisions are made. However, with the use of these systems, many erroneous results emerged. Technologies have started to be developed to tackle unexpected results. As for the solution to the issue, companies generally focus on algorithm-oriented errors. The utilized solutions usually only work in some algorithms. Because the cause of the problem is not just the algorithm; it is also the data itself. For instance, deep learning cannot establish the cause-effect relationship quickly. In addition, the boundaries between statistical or heuristic algorithms are unclear. The algorithm's fairness may vary depending on the data related to context. From this point of view, our article focuses on how the data should be, which is not a matter of statistics. In this direction, the picture in question has been revealed through a scenario specific to "vulnerable and disadvantaged" groups, which is one of the most fundamental problems today. With the joint contribution of computer science and social sciences, it aims to predict the possible social dangers that may arise from artificial intelligence algorithms using the clues obtained in this study. To highlight the potential social and mass problems caused by data, Gerbner's "cultivation theory" is reinterpreted. To this end, we conduct an experimental evaluation on popular algorithms and their data sets, such as Word2Vec, GloVe, and ELMO. The article stresses the importance of a holistic approach combining the algorithm, data, and an interdisciplinary assessment.This article is categorized under:Algorithmic Development > Statistics
