The increased exposure of the average citizen and customer to polarised content from various sources has been of significant consequence for companies and governmental organisations. Such content has, for example, served as a catalyst for violent uprisings and shifts in stock market prices. The collection and study of opinion have therefore become a necessity in many industries. Due to the vast nature of such data, manual approaches to this problem are no longer feasible. Several computational approaches have been proposed within the field of sentiment analysis, which successfully address many aspects of this problem, such as the classification of data into one of several sentiment categories. The research in the field is lacking, however, with respect to the integration and application of these techniques in practice, as well as their incorporation into the decision-making process of affected entities. In this paper, a generic framework for sentiment analysis is proposed, with a focus on facilitating the model development process for a user in a manner such that good performance may be achieved irrespective of the problem domain, as well as facilitating a flexible, exploratory analysis of model results in combination with existing structured attributes in order to gain actionable insights. The objective of the framework is to aid organisations in successfully leveraging unstructured, opinion-bearing data in combination with structured data sources to inform decision making.

普通公民和客户越来越多地接触来自各种来源的两极分化的内容，这对公司和政府组织产生了重大影响。例如，此类内容已成为暴力起义和股市价格变动的催化剂。因此，收集和研究观点已成为许多行业的必要条件。由于此类数据的巨大性质，手动解决此问题的方法不再可行。在情感分析领域中已经提出了几种计算方法，这些方法成功地解决了该问题的许多方面，例如将数据分类为几种情感类别之一。但是，在这些技术在实践中的集成和应用方面，尚缺乏该领域的研究，以及将其纳入受影响实体的决策过程中。在本文中，提出了一种用于情感分析的通用框架，其重点在于以某种方式促进用户的模型开发过程，从而无论问题领域如何都可以实现良好的性能，并促进灵活的探索性分析模型结果与现有结构化属性的组合，以获得可行的见解。该框架的目的是帮助组织成功地利用非结构化的，带有意见的数据以及结构化的数据源，为决策提供依据。侧重于以某种方式促进用户的模型开发过程，使得无论问题域如何都可以实现良好的性能，以及促进对模型结果与现有结构化属性进行灵活，探索性的分析，从而获得可行的见解。该框架的目的是帮助组织成功地利用非结构化的，带有意见的数据以及结构化的数据源，为决策提供依据。侧重于以某种方式促进用户的模型开发过程，使得无论问题域如何都可以实现良好的性能，以及促进对模型结果与现有结构化属性进行灵活，探索性的分析，从而获得可行的见解。该框架的目的是帮助组织成功地利用非结构化的，带有意见的数据以及结构化的数据源，为决策提供依据。