Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose “The lockdown: One month in Wuhan” YouTube documentary, and applied emotion analysis as well as a (...) machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust, joy, and anticipation are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing. (shrink)

More than three billion users are currently on one of Meta’s online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in more digital footprints to be studied and monetized. In this context, it is debated if social media platforms can elicit addictive behaviors. In the present work, we demonstrate in N (...) = 1,094 users that it is possible to predict from digital footprints of the Facebook users their self-reported addictive tendencies toward social media by applying machine-learning strategies. More specifically, we analyzed the predictive power of a set of models based on different sets of features extracted from digital traces, namely posting activity, language use, and page Likes. To maximize the predictive power of the models, we used an ensemble of linear and non-linear prediction algorithms. This work showed also sufficient accuracy rates in distinguishing between disordered and non-disordered social media users. In sum, individual differences in tendencies toward “social networks use disorder” can be inferred from digital traces left on the social media platform Facebook. Please note that the present work is limited by its cross-sectional design. (shrink)

Emojis and stickers are becoming increasingly popular in computer mediated communications. The present study examined the associations between personality traits and people’s reasons and patterns for using both emojis and stickers. Participants (n= 312) completed three on-line questionnaires assessing shyness, the Big Five personality traits and why and how they used emojis and stickers. Results revealed that shyness, neuroticism, extraversion and agreeableness were correlated with different reasons of usage. Moreover, some participants exhibited a tendency to adjust frequency of usage depending (...) on who the target person was and whether they were in a private or group chat. People who showed such tendencies were found to differ in personality with those who did not. Some differences in usage patterns were also observed between emojis and stickers. Together, the present study sheds more insights into how emojis and stickers can help people with different personality traits to achieve different purposes in their daily communication. (shrink)

ABSTRACT The ubiquity of social media usage has led to exciting new technologies such as machine learning. Machine learning is poised to change many fields of health, including psychology. The wealth of information provided by each social media user in combination with machine-learning technologies may pave the way for automated psychological assessment and diagnosis. Assessment of individuals’ social media profiles using machine-learning technologies for diagnosis and screening confers many benefits ; however, the implementation of these technologies will pose unique challenges (...) to the professional ethics of psychology. Namely, psychologists must understand the impact of these assessment technologies on privacy and confidentiality, informed consent, recordkeeping, bases for assessments, and diversity and justice. This paper offers a brief review of the current applications of machine-learning technologies in psychology and public health, provides an overview of potential implementations in clinical settings, and introduces ethical considerations for professional psychologists. This paper presents considerations which may aid in the extension of the current Ethical Principles of Psychologists and Code of Conduct to address these important technological advancements in the field of clinical psychology. (shrink)

Cultural development is often reflected in the emotional expression of various cultural carriers, such as literary works, movies, etc. Therefore, the cultural development can be analyzed through emotion analysis of the text, so as to sort out its context and obtain its development dynamics. This paper proposes a text emotion analysis method based on deep learning. The traditional neural network model mainly deals with the classification task of short texts in the form of word vectors, which causes the model to (...) rely too much on the accuracy of word segmentation. In addition, the short texts have the characteristics of short corpus and divergent features. A text emotion classification model combing the Bidirectional Encoder Representations from Transformers and Bi-directional Long Short-Term Memory is developed in this work. First, the BERT model is used to convert the trained text into a word-based vector representation. Then, the generated word vector is employed as the input of the BiLSTM to obtain the semantic representation of the context of the relevant word. By adding random dropout, the mechanism prevents the model from overfitting. Finally, the extracted feature vector is input to the fully connected layer, and the emotion category to which the text belongs is calculated through the Softmax function. Experiments show that in processing short texts, the proposed model based on BERT-BiLSTM is more accurate and reliable than the traditional neural network model using word vectors. The proposed method has a better analysis effect on the development of western culture. (shrink)

A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they (...) have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic. (shrink)