Sentic computing is a multi-disciplinary approach to sentiment analysis at the crossroads between affective computing and commonsense computing, which exploits both computer and social sciences to better recognize, interpret, and process opinions and sentiments over the Web. In the last ten years, many different models (such as the Hourglass of Emotions and Sentic Patterns), resources (such as AffectiveSpace and SenticNet), algorithms (such as Sentic LDA and Sentic LSTM), and applications (such as Sentic PROMs and Sentic Album) have been developed under the umbrella of sentic computing. In this paper, we review all such models, resources, algorithms, and applications together with the key shifts and tasks introduced by sentic computing in the context of affective computing and sentiment analysis. We also discuss future directions in these fields.

情感计算是情感计算和常识计算之间交汇处的多学科情感分析方法，它利用计算机和社会科学来更好地识别，解释和处理Web上的观点和情感。在过去的十年中，许多不同的模型（例如，情绪和沙漏模式的沙漏），资源（例如，AffectiveSpace和SenticNet），算法（例如，Sentic LDA和Sentic LSTM）以及应用程序（例如，Sentic PROM和Sentic）专辑）已在情感计算的保护下进行了开发。在本文中，我们将回顾所有这些模型，资源，算法和应用程序，以及在情感计算和情感分析的背景下由情感计算引入的关键转变和任务。我们还将讨论这些领域的未来发展方向。