With the rising number of Internet users, there has been a rapid increase in cyberbullying. Among the types of cyberbullying, verbal abuse is emerging as the most serious problem, for preventing which profanity is being identified and blocked. However, users employ words cleverly to avoid blocking. With the existing profanity discrimination methods, deliberate typos and profanity using special characters can be discriminated with high accuracy. However, as they cannot grasp the meaning of the words and the flow of sentences, standard words such as “Sibaljeom (starting point, a Korean word that sounds similar to a swear word)” and “Saekkibalgalag (little toe, a Korean word that sounds similar to another swear word)” are less accurately discriminated. Therefore, in order to solve this problem, this study proposes a method of discriminating profanity using a deep learning model that can grasp the meaning and context of words after separating Hangul into the onset, nucleus, and coda.

Recently, multimodal approaches that combine various modalities have been attracting attention to recognizing emotions more accurately. Although multimodal fusion delivers strong performance, it is computationally intensive and difficult to handle in real time. In addition, there is a fundamental lack of large-scale emotional datasets for learning. In particular, Korean emotional datasets have fewer resources available than English-speaking datasets, thereby limiting the generalization capability of emotion recognition models. In this study, we propose a more lightweight modality fusion method, MMER-LMF, to overcome the lack of Korean emotional datasets and improve emotional recognition performance while reducing model training complexity. To this end, we suggest three algorithms that fuse emotion scores based on the reliability of each model, including text emotion scores extracted using a pre-trained large-scale language model and video emotion scores extracted based on a 3D CNN model. Each algorithm showed similar classification performance except for slight differences in disgust emotion performance with confidence-based weight adjustment, correlation coefficient utilization, and the Dempster–Shafer Theory-based combination method. The accuracy was 80% and the recall was 79%, which is higher than 58% using text modality and 72% using video modality. This is a superior result in terms of learning complexity and performance compared to previous studies using Korean datasets.

Computer communication via text messaging or Social Networking Services (SNS) has become increasingly popular. At this time, many studies are being conducted to analyze user information or opinions and recognize emotions by using a large amount of data. Currently, the methods for the emotion recognition of dialogues requires an analysis of emotion keywords or vocabulary, and dialogue data are mostly classified as a single emotion. Recently, datasets classified as multiple emotions have emerged, but most of them are composed of English datasets. For accurate emotion recognition, a method for recognizing various emotions in one sentence is required. In addition, multi-emotion recognition research in Korean dialogue datasets is also needed. Since dialogues are exchanges between speakers. One’s feelings may be changed by the words of others, and feelings, once generated, may last for a long period of time. Emotions are expressed not only through vocabulary, but also indirectly through dialogues. In order to improve the performance of emotion recognition, it is necessary to analyze Emotional Association in Dialogues (EAD) to effectively reflect various factors that induce emotions. Therefore, in this paper, we propose a more accurate emotion recognition method to overcome the limitations of single emotion recognition. We implement Intrinsic Emotion Recognition (IER) to understand the meaning of dialogue and recognize complex emotions. In addition, conversations are classified according to their characteristics, and the correlation between IER is analyzed to derive Emotional Association in Dialogues (EAD) and apply them. To verify the usefulness of the proposed technique, IER applied with EAD is tested and evaluated. This evaluation determined that Micro-F1 of the proposed method exhibited the best performance, with 74.8% accuracy. Using IER to assess the EAD proposed in this paper can improve the accuracy and performance of emotion recognition in dialogues.

Due to the recent increase in non-face-to-face services due to COVID-19, the number of users communicating through messengers or SNS (social networking service) is increasing. As a large amount of data is generated by users, research on recognizing emotions by analyzing user information or opinions is being actively conducted. Conversation data such as SNS is freely created by users, so there is no set format. Due to these characteristics, it is difficult to analyze using AI (artificial intelligence), which leads to a decrease in the performance of the emotion recognition technique. Therefore, a processing method suitable for the characteristics of unstructured data is required. Among the unstructured data, most emotion recognition in Korean conversation recognizes a single emotion by analyzing emotion keywords or vocabulary. However, since multiple emotions exist complexly in a single sentence, research on multilabel emotion recognition is needed. Therefore, in this paper, the characteristics of unstructured conversation data are considered and processed for more accurate emotion recognition. In addition, we propose a multilabel emotion recognition technique that understands the meaning of dialogue and recognizes inherent and complex emotions. A deep learning model was compared and tested as a method to verify the usefulness of the proposed technique. As a result, performance was improved when it was processed in consideration of the characteristics of unstructured conversation data. Also, when the attention model was used, accuracy showed the best performance with 65.9%. The proposed technique can contribute to improving the accuracy and performance of conversational emotion recognition.