Opinion Stream Classification with Ensembles and Active leaRners - OSCAR

随着WEB 2.0的兴起，许多人使用社交媒体发布几乎任何主题的意见-事件，产品，主题。意见挖掘用于得出人们对每个主题的态度的结论;这种见解对于产品设计和广告，活动策划，政治运动等是必不可少的。变化涉及对一个主题的一般情绪和对这个主题的具体方面，以及用来表达情绪的词语。主题也会随着时间的推移而变化。在OSCAR中，我们将开发意见流挖掘方法来处理变化并不断适应学习的模型。OSCAR的第一部分是利用流挖掘方法来处理词汇变化。在文本挖掘中，词汇构成了特征空间。特征空间的变化意味着建立在旧单词上的模型必须更新。每当一个新单词出现或一个单词不再使用时进行这样的更新是不切实际的。在OSCAR中，我们将收集每个单词的用法和情感信息，以突出单词极性和文档极性之间的长期相互作用。在此基础上，我们将设计评估单词重要性的方法，用于模型适应，通过只使用在一段时间内仍然重要的单词来更新词汇表，并逐渐适应模型。第二，我们将努力减少对标记文档的需求。在流分类中，假设专家在任何时候都可以标记到达的数据实例。这种假设在主动学习中被放弃了，在主动学习中，只有少数实例被选择用于标记-那些期望最能改善模型的实例。主动学习方法假设一个固定的特征空间。在OSCAR中，我们将开发主动流学习方法，在不断发展的特征空间上学习和适应极性模型。第三，我们将同时处理不同类型的变化。为了这个目的，我们将使用合奏。我们将致力于一些合奏成员的主题趋势的识别，其他的词汇和其他时间的变化，包括周期性的变化。OSCAR的输出将是一个完整的框架，包括主动集成学习方法，这些方法可以处理不同形式的变化，并在有限的专家参与下学习。该框架还将包含协调组件，这些组件将衡量单个模型对最终模型的贡献，并调节集合成员和主动学习者之间的信息交换。我们将在真实的数据上测试OSCAR，主要来自Twitter：我们将研究词汇变化和主题如何在特定主题领域的推文流中出现和消失，以及它们如何影响学习模型。

With the rise of WEB 2.0, many people use social media to post opinions on almost any subject - events, products, topics. Opinion mining is used to draw conclusions on the attitude of people towards each subject; such insights are essential for product design and advertisement, for event planning, political campaigns etc. As opinions accumulate, though, changes occur and invalidate the models from which these conclusions are drawn. Changes concern the general sentiment towards a subject and towards specific facets of this subject, as well as the words used to express sentiment. Subjects also change over time. In OSCAR, we will develop opinion stream mining methods that deal with change and adapt the learned models continuously.The first part of OSCAR is on leveraging stream mining methods to deal with vocabulary changes. In text mining, the vocabulary words constitute the feature space. A change in the feature space means that the model built upon the old words must be updated. It is impractical to do such an update whenever a new word appears or a word gets out of use. In OSCAR, we will rather accumulate information on the usage and sentiment of each word to highlight the long-term interplay between word polarity and document polarity. On this basis, we will design methods that assess the importance of a word for model adaptation, update the vocabulary by using only words that remain important for some time, and adapt models gradually.Second, we will work on reducing the need for labeled documents. In stream classification, it is assumed that an expert is available at any time to label the arriving data instances. This assumption is waived in active learning, where only few instances are chosen for labeling - those expected to improve the model the most. Active learning methods assume a fixed feature space. In OSCAR, we will develop active stream learning methods that learn and adapt polarity models on an evolving feature space.Third, we will work on dealing with different types of change simultaneously. To this purpose, we will use ensembles. We will dedicate some ensemble members to the identification of topic trends, others to changes in the vocabulary and others to temporal changes, including periodical ones. We will investigate ways of coordinating the ensemble members to ensure a smooth adaption of the final ensemble model at any time.The output of OSCAR will be a complete framework, encompassing active ensemble learning methods that deal with different forms of change and learn with limited expert involvement. The framework will also encompass coordinating components that weigh the contribution of individual models to the final one, and regulate the exchange of information between ensemble members and active learners.We will test OSCAR on real data, mainly from Twitter: we will study how vocabulary changes and topics emerge and fade in streams of tweets for specific subject areas, and how they influence the learned model.