With the development of modern society and the improvement of living standards,care for special needs children has been increasingly highlighted,and numerous corresponding measures such as welfare homes,special educat...With the development of modern society and the improvement of living standards,care for special needs children has been increasingly highlighted,and numerous corresponding measures such as welfare homes,special education schools,and youth care centers have emerged.Due to the lack of systematic emotional companionship,the mental health of special needs children are bound to be affected.Nowadays,emotional education,analysis,and evaluation are mostly done by psychologists and emotional analysts,and these measures are unpopular.Therefore,many researchers at home and abroad have focused on the solution of psychological issues and the psychological assessment and emotional analysis of such children in their daily lives.In this paper,a special children’s psychological emotional analysis based on neural network is proposed,where the system sends the voice information to a cloud platform through intelligent wearable devices.To ensure that the data collected are valid,a series of pretreatments such as Chinese word segmentation,de-emphasis,and so on are put into the neural network model.The model is based on the further research of transfer learning and Bi-GRU model,which can meet the needs of Chinese text sentiment analysis.The completion rate of the final model test has reached 97%,which means that it is ready for use.Finally,a web page is designed,which can evaluate and detect abnormal psychological state,and at the same time,a personal emotion database can also be established.展开更多
Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech a...Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.展开更多
文摘With the development of modern society and the improvement of living standards,care for special needs children has been increasingly highlighted,and numerous corresponding measures such as welfare homes,special education schools,and youth care centers have emerged.Due to the lack of systematic emotional companionship,the mental health of special needs children are bound to be affected.Nowadays,emotional education,analysis,and evaluation are mostly done by psychologists and emotional analysts,and these measures are unpopular.Therefore,many researchers at home and abroad have focused on the solution of psychological issues and the psychological assessment and emotional analysis of such children in their daily lives.In this paper,a special children’s psychological emotional analysis based on neural network is proposed,where the system sends the voice information to a cloud platform through intelligent wearable devices.To ensure that the data collected are valid,a series of pretreatments such as Chinese word segmentation,de-emphasis,and so on are put into the neural network model.The model is based on the further research of transfer learning and Bi-GRU model,which can meet the needs of Chinese text sentiment analysis.The completion rate of the final model test has reached 97%,which means that it is ready for use.Finally,a web page is designed,which can evaluate and detect abnormal psychological state,and at the same time,a personal emotion database can also be established.
文摘Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.
文摘为提升客户对产品的认可度,针对传统概念设计阶段未充分考虑客户感性需求偏好及客户感性需求获取困难的问题,提出一种在线评论数据驱动的客户感性需求识别及向设计特征映射方法。首先,基于形态学分析法,通过构建能量材料信号(Energy Material Signal, EMS)模型试图从产品中发现所有设计特征,并基于在线评论数据从所有设计特征中筛选出用户关注的核心特征;其次,从在线评论数据中提取形容词组成感性词对,以感性词作为中心词并利用词向量技术获得非中心词,基于情感词典,利用所给方法计算产品感性词对的感性评价值;然后,基于数量化理论Ⅰ(Quantitative TheoryⅠ,QTⅠ)建立客户感性评价与产品设计元素之间的映射模型,并为改进产品设计提供依据;最后,通过实例验证所提方法的可行性和有效性。
