Hybrid Model for Stress Detection in Social Media by Using Dynamic Factor Graph Model and Convolutional Neural Networks
01 Jan 2021-pp 101-107
TL;DR: In this article, a Dynamic Factor Graph model (DFGM) is combined with CNN to utilize tweets and group cooperation information for stress disclosure and furthermore, the most part of FGM focuses on mood cast technique which is dependent on unique persistent in demonstrating and predicts the user's emotions in social life.
Abstract: In present days, mental pressure is getting to be real medical problems. It distinguishes to recognize pressure opportune in proactive way. By extending the predominance digital life, person using to sharing their step-by-step activities and speaking to companions by means of electronic systems administration locales in various ways, so it makes viable to utilize online electronic life data for stress acknowledgment. Here, we discover clients' stress state which is immovably related to that of companions in their Web/public activity, and also, we use different sizes of dataset in various states which is identified with public activity to productively look at the relationship of clients' stress states and connecting within public activity. First, we identify the clients' stress states like visual pictures, writings and group properties with alternate points of view and further move on novel model to show. Dynamic factor graph model (DFGM) is combined with convolutional neural network (CNN) to utilize tweets and group cooperation information for stress disclosure and furthermore which can identify some dependencies locally and scale of invariance in discourse of speech recognitions and also in image recognition (i.e., smileys). The most part of FGM focuses on mood cast technique which is dependent on unique persistent in demonstrating and predicts the user’s emotions in social life. By further examining the social life of the user’s data, we can likewise find many scenarios and the amount of social models in form of scattered connection of stress related to users and also with non-stressed users.
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TL;DR: Wang et al. as mentioned in this paper designed two techniques for category-aware chronic stress detection: (1) a stress-oriented word embedding on the basis of an existing pre-trained word embeddings, aiming to strengthen the sensibility of stress-related expressions for linguistic post analysis; (2) a multi-attention model with three layers (i.e., category-attention layer, posts selfattention layer, and category-specific post attention layer), aiming to capture inter-relevance from a sequence of posts and infer long-term stress categories and stress levels.
Abstract: People today live a stressful life. Compared with acute stress, long-term chronic stress is more harmful, and may cause or exacerbate many serious health problems, including high blood pressure, heart disease, chronic pain, and mental diseases. With social media becoming an integral part of our daily lives for information sharing and self-expression, detecting category-aware long-standing chronic stress from a large volume of historic open posts made by social media users is possible. In this study, we construct a data set containing 971 chronically stressed users with totally 54 546 open posts on Sina microblog from July 5, 2018 to December 1, 2019, and design two techniques for category-aware chronic stress detection: (1) a stress-oriented word embedding on the basis of an existing pre-trained word embedding, aiming to strengthen the sensibility of stress-related expressions for linguistic post analysis; (2) a multi-attention model with three layers (i.e., category-attention layer, posts self-attention layer, and category-specific post attention layer), aiming to capture inter-relevance from a sequence of posts and infer long-term stress categories and stress levels. The experimental results show that the proposed multi-attention model equipped with the stress-oriented word embedding can achieve 80.65% accuracy in detecting category-aware stress levels, 86.49% accuracy in detecting chronic stress levels only, and 93.07% accuracy in detecting chronic stress categories only. Limitations and implications of the study are also discussed at the end of the paper.
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TL;DR: MoodLens is the first system for sentiment analysis of Chinese tweets in Weibo, and by using the highly efficient Naive Bayes classifier, MoodLens is capable of online real-time sentiment monitoring.
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261 citations
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