Consumer perception of extruded snacks containing brown rice and dried mushroom

Effects of mushroom type, seasoning and health benefit information (HBI) on consumers’ saltiness expectation, sensory liking, elicited emotions and purchase intent (PI) of extruded snacks were investigated. Five snacks were evaluated: straw mushroom (Volvariella volvacea) extrudates without (SME) or with seasoning (SMES), phoenix mushroom (Pleurotus pulmonarius) extrudates without (PME) or with seasoning (PMES), and the control without mushroom and seasoning. Hedonic scores and positive emotions were generally higher for seasoned mushroom-containing snacks (SMES and PMES) with 65% and 75.83% of consumers reporting willingness to purchase, respectively, after receiving HBI. Bored, interested and satisfied were identified as significant emotional predictors for PI odds. Flavour, saltiness, overall liking, bored, good and interested were critical attributes, differentiating snacks. This study demonstrated that sensory liking and PI of extruded brown rice-based snacks containing mushroom could be improved through savoury seasoning addition, which also allowed saltiness expectations to be met.     

面向智能客服系统的情感分析技术

该文以阿里小蜜为例,对智能客服系统中的情感分析技术进行比较全面的介绍,包括情感分析算法模型的原理及其在智能客服系统的多个应用场景中的实际落地使用方式和效果分析。智能客服在解决客户高频业务问题的同时,也需要给客户提供多维度的、具有类人能力的助理、导购、语聊和娱乐等服务,提高客户对智能客服机器人的整体满意度。在此过程中,情感分析技术在机器人类人能力建设中起到了至关重要的作用。该文围绕智能客服系统中人机结合的服务形式,从六个维度总结和介绍了情感分析技术在智能客服系统中的应用场景,包括用户情感检测、用户情感安抚、情感生成式语聊、客服服务质检、会话满意度预估和智能人工入口。     

EmotionMap: Visual Analysis of Video Emotional Content on a Map

Journal of Computer Science and Technology - Emotion plays a crucial role in gratifying users’ needs during their experience of movies and TV series, and may be underutilized as a framework...     

Neural Emotion Detection via Personal Attributes

There has been a recent line of work to automatically detect the emotions of posts in social media. In literature, studies treat posts independently and detect their emotions separately. Different from previous studies, we explore the dependence among relevant posts via authors' backgrounds, since the authors with similar backgrounds, e.g., "gender", "location", tend to express similar emotions. However, personal attributes are not easy to obtain in most social media websites. Accordingly, we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection: the Joint Model with Personal Attention Mechanism (JPA) model is used to detect emotion and personal attributes jointly, and capture the attributes-aware words to connect similar people; the Neural Personal Discrimination (NPD) model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection. Experimental results show the usefulness of personal attributes in emotion detection, and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.     

环境、情感、文化“嫁接”与建筑创作

西南是中国西部乡土建筑众多的区域,文化的积淀是多彩的,它对现代化新建筑创作、对乡土建筑文化的发展有着深远影响和坚实的根基,本文依西南乡土建筑文化,引东部民间建筑及国外建筑,通过对建筑文化及环境、情感的探讨,进而说明建筑创作应立足于本土建筑文化,同时与外来优秀建筑文化、思想观念“嫁接”,必然产生积极后果。建筑创作应是环境、情感及文化“嫁接”三者关系的产物,是方法,又是原则。     

建筑施工企业情感管理

针对建筑施工企业的性质和特点,提出了情感管理的原则,说明了情感管理的重要性,以及如何进行情感管理,达到提高企业管理水平的效果,以充分激发员工的工作热情,使人才更好地服务于企业。     

园游、郊游、山水游：三种时空尺度 北宋文人交游

中国古代文人的群体一致性与个体矛盾性并存，该特征的直接表现形式是文人交游活动，园林因作为交游活动的场所而具有历史传承意义。然而此类研究集中于文、史、哲、艺等学科，风景园林对其关注度较低。北宋文人群体规模大、雅集结社之风盛行，且交游唱和诗作遗存较多。因此，以北宋文人群体为研究对象，以司马光的独乐园及其举办的真率会、欧阳修春游嵩山，以及邵雍的一次长途秋游为例，对园记、交游唱和诗等遗存进行文本分析，归纳总结了园游、郊游、山水游3种不同时空尺度的文人交游活动，挖掘文人交游与山水园林调和共生、精神反哺的关系，对风景园林历史理论体系的完善具有积极意义，观古知今以实现古为今用。     

建筑的情感创作与情感阅读

通过对情感在建筑中传达与被阅读过程的描述，指出情感表达对建筑艺术创作有非常重要的意义。最后以安腾和巴拉干的建筑作品为例探讨如何用情感营造诗意空间。     

“非先锋”的先锋(上)——阿道夫·路斯及其现代性研究

从去除装饰问题出发,本文分析了20世纪初维也纳建筑师阿道夫·路斯复杂而深刻的建筑理论、建筑实践和文化批判思想。此外,文章还讨论了路斯独特的现代性理解及其独特的人物性格,并在现代性的视野下梳理了路斯的建筑思想和历史意义。     

基于情感词典与LDA模型的股市文本情感分析

建立了一种基于股票情感词典与LDA分析股票文本情感倾向的模型。针对股票文本情感分析中情感词典不全面与句子分析片面的问题,构建较为全面的股票情感词典,同时以句子的倾向性、程度性与相关性三方面分析股票文本情感。引入针对股票的词语、程度性词语与转折性词语构建较为全面的情感词典;抽取预处理之后的股票文本句子的情感词;利用句子算法计算句子倾向、程度向量,并对句子向量利用支持向量机(SVM)和K均值算法分类;利用LDA(latent dirichlet allocation)对情感词计算文档 主题、文档 词语概率分布,以此概率分布获取句子的相关性;综合句子的倾向性、程度性、相关性计算句子情感;最后,通过句子情感获取股票文本的情感倾向比例。通过对百度新闻经济板块收集的股票文本进行实验并与其他算法比较,该模型对句子与文本分类准确率提高到82.78%与84.14%。