A standardized database of Chinese emotional short videos based on age and gender differences

PLOS ONE RESEARCH ARTICLE A standardized database of Chinese emotional short videos based on age and gender differences Danting Duan1, Wei Zhong2, Shuang Ran1, Long Ye ID2*, Qin Zhang2 1 Key Laboratory of Media Audio & Video, Communication University of China, Beijing, China, 2 State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Duan D, Zhong W, Ran S, Ye L, Zhang Q (2023) A standardized database of Chinese emotional short videos based on age and gender differences. PLoS ONE 18(3): e0283573. https:// doi.org/10.1371/journal.pone.0283573 Editor: Yuvaraj Rajamanickam, Nanyang Technological University, SINGAPORE Received: October 12, 2022 Accepted: March 9, 2023 Published: March 30, 2023 Copyright: © 2023 Duan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The supporting information of this paper can be downloaded at the following link, https://github.com/EEG-EmotionRecognition-group/A-standardized-database-ofChinese-emotional-short-videos-based-on-ageand-gender-differences.git. Funding: This work was supported by the National Natural Science Foundation of China (No. 62271455), and the Fundamental Research Funds for the Central Universities (No. CUC18LG024). The funder of the National Natural Science Foundation of China (No. 62271455) has taken the role of * Abstract Most of the existing emotion elicitation databases use the film clips as stimuli and do not take into account the age and gender differences of participants. Considering the short videos have the advantages of short in time, easy to understand and strong emotional appeal, we choose them to construct a standardized database of Chinese emotional short videos by the joint analysis of age and gender differences. Two experiments are performed to establish and validate our database. In the Experiment 1, we selected 240 stimuli from 2700 short videos and analyzed the subjective evaluation results of 360 participants with different ages and genders. As a result, a total of 54 short videos with three categories of emotions were picked out for 6 groups of participants, including the male and female respectively aged in 20-24, 25-29 and 30-34. In the Experiment 2, we recorded the EEG signals and subjective experience scores of 81 participants while watching different video stimuli. Both the results of EEG emotion recognition and subjective evaluation indicate that our database of 54 short videos can achieve better emotion elicitation effects compared with film clips. Furthermore, the targeted delivery of specific short videos has also been verified to be effective, helping the researchers choose appropriate emotional elicitation stimuli for different participants and promoting the study of individual differences in emotion responses. Introduction Emotion has always been a hot research topic in the fields of psychology and artificial intelligence [1–3]. As an essential step in affective computing, emotion elicitation has also drawn increasing attention [4]. Therefore, creating the effective emotion elicitation databases is becoming a popular topic for researchers interested in emotion. Currently, the researchers have attempted a range of methods of eliciting emotion in the laboratory, such as interactive training, hypnosis, pictures, music, slides and film clips [5–7]. Compared to other emotional stimuli, the film clips (i.e., a portion of a full-length film) exhibit several advantages in emotion eliciting tasks. Firstly, the film clips are dynamic stimuli with both auditory and visual channels, which can highly attract attention [8]. Secondly, they have PLOS ONE | https://doi.org/10.1371/journal.pone.0283573 March 30, 2023 1 / 29 PLOS ONE funding acquisition and project administration. The funder of the Fundamental Research Funds for the Central Universities (No. CUC18LG024) has taken the role of conceptualization, funding acquisition, investigation and writing (review and editing). Competing interests: The authors have declared that no competing interests exist. None relatively high ecological validity and can induce strong subjective experiences and physiological changes [5]. Thirdly, the film clips present continuous emotional scenes and are able to capture the emotions that develop over time [9]. Furthermore, the meta-analysis of emotion elicitation has also validated that film clips are one of the most effective ways to elicit emotion [10]. Generally, the selection of film clips for emotion eliciting tasks should follow three criteria [5]: relatively short duration, comprehensible without additional explanation, and inducing a specific target emotion. For the construction of emotional film databases, a lot of efforts have been made over the years. According to the emotion model [11], the existing emotional film databases can be divided into two categories, the dimensional model-based and the discrete model-based. The dimensional model believes that emotions are characterized by combinations of dimensions, such as valence, arousal, and dominance [12]. Baveye et al. [13] adopted a crowdsourcing approach by asking annotators to rate the degree of valence and arousal, and built a large free-shared database of 9,800 film clips. Zheng et al. [14] edited 15 film clips from 6 films, and divided their emotions into three categories (positive, neutral and negative) along the valence dimension. Ismail et al. [15] displayed 24 videos to 42 participants by an online survey, and the rating results of valence-arousal dimensions indicated that 79 percent of the videos could successfully elicit the target emotion. While the discrete model suggests that emotions can be classified into basic categories, and the first discrete emotional film database was developed in [16] to elicit six basic emotions proposed by Ekman [17]. Thereafter, Gross and Levenson [5] creatively proposed the concept of success index to quantify the effect of film clips on eliciting emotions and constructed a film database that can induce eight emotions. To provide more immersive experience, Jeong et al. [18] built a database of 4D films with the aid of chair movements, vibrations, winds and scents. It is worth noting that the participants in different cultures and languages will have different responses to the same emotional stimulus [19–21]. Therefore, the researchers have also created different databases for different cultures and languages. Michelini et al. [22] designed a film database for LatinAmericans, where the emotional states can be analyzed from both dimensional and discrete perspectives. Shalchizadeh et al. [23] recorded the emotional responses of 88 participants by means of (...truncated)