This paper proposes a new aggregated classification scheme aimed to support the implementation of semantic text analysis methods in contexts characterized by the presence of rare text categories. The proposed approach starts from the aggregate supervised text classifier developed by Hopkins and King and moves forward, relying on rare event sampling methods. In detail, it enables the analyst to enlarge the number of estimated sentiment categories, both preserving the estimation accuracy and reducing the working time to unconditionally increase the size of the training set. The approach is applied to study the daily evolution of the web reputation of one of the last mega-event taking place in Europe: Expo Milano. The corpus consists of more than one million tweets in both Italian and English, discussing about the event. The analysis provides an interesting portrayal of the evolution of the Expo stakeholders’ opinions over time and allows the identification of the main drivers of the Expo reputation. The algorithm will be implemented as a running option in the next release of the R package ReadMe.

本文提出了一种新的聚合分类方案，旨在支持在以稀有文本类别为特征的上下文中实施语义文本分析方法。提议的方法从Hopkins和King开发的聚合监督文本分类器开始，然后依靠罕见事件采样方法向前发展。详细地说，它使分析人员能够扩大估计的情感类别的数量，既可以保持估计的准确性，又可以减少工作时间以无条件地增加训练集的大小。该方法用于研究欧洲最近发生的大型活动之一：米兰世博会的网络声誉的每日演变。该语料库包含超过一百万条用意大利语和英语发布的推文，讨论了该事件。该分析为世博会利益相关者的意见随时间的演变提供了有趣的刻画，并有助于确定世博会声誉的主要驱动力。该算法将在R软件包自述文件的下一个版本中作为运行选项实现。