RESUMEN
BACKGROUND AND OBJECTIVE: Depression can severely impact physical and mental health and may even harm society. Therefore, detecting the early symptoms of depression and treating them on time is critical. The widespread use of social media has led individuals with depressive tendencies to express their emotions on social platforms, share their painful experiences, and seek support and help. Therefore, the massive available amounts of social platform data provide the possibility of identifying depressive tendencies. METHODS: This paper proposes a neural network hybrid model MTDD to achieve this goal. Analysis of the content of users' posts on social platforms has facilitated constructing a post-level method to detect depressive tendencies in individuals. Compared with existing methods, the MTDD model uses the following innovative methods: First, this model is based on social platform data, which is objective and accurate, can be obtained at a low cost, and is easy to operate. The model can avoid the influence of subjective factors in the depressive tendency detection method based on consultation with mental health experts. In other words, it can avoid the problem of undisclosed and imperfect data in depressive tendency detection. Second, the MTDD model is based on a deep neural network hybrid model, combining the advantages of CNN and BiLSTM networks and avoiding the problem of poor generalization ability in a single model for depression tendency recognition. Third, the MTDD model is based on multimodal features for learning the vector representation of depression-prone text, including text features, semantic features, and domain knowledge, making the model more robust. RESULTS: Extensive experimental results demonstrate that our MTDD model detects users who may have a depressive tendency with a 95% F1 value and obtained SOTA results. CONCLUSIONS: Our MTDD model can detect depressive users on social media platforms more effectively, providing the possibility for early diagnosis and timely treatment of depression. The experiment proves that our MTDD model outperforms many of the latest depressive tendency detection models.
Asunto(s)
Depresión , Medios de Comunicación Sociales , Humanos , Depresión/diagnóstico , Emociones , Semántica , Salud MentalRESUMEN
In recent years, named entity recognition (NER) has attracted significant attention in various fields, especially in the clinical medical field, because NER is essential for useful mining knowledge in the clinical medical area. However, there are still some problems in Chinese named entity recognition, such as the complexity of medical texts, word segmentation errors, and incomplete extraction of semantic information. In this paper, we propose a Chinese NER method based on the multi-granularity semantic dictionary and multimodal tree method, which involves the following steps. First, we extract different semantic words using multimodal trees. Next, we extract the boundary information, and finally, perform the multi-granularity feature fusion. Furthermore, we combine the above methods to complete the entity recognition task. From the results of our experimental verification, our proposed model outperforms the current state-of-the-art results.