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Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications
Cognitive Computation ( IF 5.4 ) Pub Date : 2021-01-30 , DOI: 10.1007/s12559-021-09839-4
Albert Weichselbraun 1, 2 , Jakob Steixner 3 , Adrian M P Braşoveanu 3 , Arno Scharl 2, 4 , Max Göbel 2 , Lyndon J B Nixon 3, 4
Affiliation  

Sentic computing relies on well-defined affective models of different complexity—polarity to distinguish positive and negative sentiment, for example, or more nuanced models to capture expressions of human emotions. When used to measure communication success, even the most granular affective model combined with sophisticated machine learning approaches may not fully capture an organisation’s strategic positioning goals. Such goals often deviate from the assumptions of standardised affective models. While certain emotions such as Joy and Trust typically represent desirable brand associations, specific communication goals formulated by marketing professionals often go beyond such standard dimensions. For instance, the brand manager of a television show may consider fear or sadness to be desired emotions for its audience. This article introduces expansion techniques for affective models, combining common and commonsense knowledge available in knowledge graphs with language models and affective reasoning, improving coverage and consistency as well as supporting domain-specific interpretations of emotions. An extensive evaluation compares the performance of different expansion techniques: (i) a quantitative evaluation based on the revisited Hourglass of Emotions model to assess performance on complex models that cover multiple affective categories, using manually compiled gold standard data, and (ii) a qualitative evaluation of a domain-specific affective model for television programme brands. The results of these evaluations demonstrate that the introduced techniques support a variety of embeddings and pre-trained models. The paper concludes with a discussion on applying this approach to other scenarios where affective model resources are scarce.



中文翻译:

用于 Web 智能应用程序的特定领域情感模型的自动扩展

情感计算依赖于定义明确的不同复杂性的情感模型——例如区分正面和负面情绪的极性,或者更细微的模型来捕捉人类情感的表达。当用于衡量沟通成功时,即使是最精细的情感模型与复杂的机器学习方法相结合,也可能无法完全捕捉到组织的战略定位目标。这样的目标常常偏离标准化情感模型的假设。虽然某些情绪(例如喜悦信任)通常代表理想的品牌联想,但营销专业人士制定的特定沟通目标往往超出这些标准维度。例如,电视节目的品牌经理可能会考虑恐惧悲伤成为观众想要的情绪。本文介绍了情感模型的扩展技术,将知识图中可用的常识和常识知识与语言模型和情感推理相结合,提高覆盖率和一致性,并支持特定领域的情感解释。一项广泛的评估比较了不同扩展技术的性能:(i)基于重新审视的情绪沙漏的定量评估模型来评估涵盖多个情感类别的复杂模型的性能,使用手动编译的黄金标准数据,以及 (ii) 对电视节目品牌的特定领域情感模型的定性评估。这些评估的结果表明,引入的技术支持各种嵌入和预训练模型。本文最后讨论了将这种方法应用于情感模型资源稀缺的其他场景。

更新日期:2021-01-31
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