III: Small: A Holistic Approach to Sentiment Analysis

情感分析的任务是提取和分类的人？在社交媒体和其他来源中表达的情感，观点和情绪。它具有非常广泛的应用，因为消费者和公众的意见，情绪表达是企业，组织和个人决策的工具。虽然情感分析在过去已经得到了广泛的研究，但它的准确性仍然很低，这限制了它的应用范围。在过去的几年里，我们和其他一些研究人员使用了一种新的机器学习范式，称为终身学习，以帮助解决情感分析的一些子问题，并取得了可喜的成果。终身学习的目的是模仿人类的学习，通过不断学习，保留/积累过去学到的知识，并使用或转移过去的知识，以帮助新的任务学习和解决问题。在这个过程中，学习者变得越来越有知识，越来越善于学习。传统的机器学习是孤立学习的，它只使用特定应用程序的数据来学习模型。这项研究将为情感分析设计更有效的原则性终身学习算法，并创建一个不断更新的属性（或方面）知识库，例如，电子产品，以及对他们的意见，并开发一个单一的框架，以显着提高情感分析的性能。为了扩大该项目的影响，成熟的技术将被转移到行业，并在课堂上演示和使用，学生将参与研究。具体来说，该项目将为情感分析的四个核心子问题开发新的终身学习算法，目标是改进：（1）通过在职学习进行方面提取，在模型构建后学习改进模型;（2）通过终身方面主题建模进行方面分组，使用过去的方面分组来帮助新的分组;（3）使用终身注意力模型的方面情感分类，其保留先前的注意力分布并利用它们为新任务构建更准确的情感分类器;以及（4）通过连续关联学习的共指消解，以发现方面和情感相关的共指关系。由此产生的算法将被纳入一个整体的终身异质任务学习模型，以显着提高情感分析的准确性。除了情感分析之外，该项目还将开发通用的终身学习算法，这些算法可以应用于基于终身情感分析获得的见解的广泛其他应用。为了传播，除了发表研究论文外，我们还将组织研讨会和提供关于项目主题的会议教程，并向研究界发布我们的注释数据和实施的软件。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Sentiment analysis is the task of extracting and classifying people?s sentiments, opinions, and emotions expressed in social media and other sources. It has a very wide range of applications because consumer and public opinions, sentiment expressions are instrumental for decision making of businesses, organizations, and individuals alike. Although sentiment analysis has been investigated extensively in the past, its accuracy is still low, which limits its scope of applications. In the past few years, we and some other researchers used a new machine learning paradigm, called lifelong learning, to help solve some sub-problems of sentiment analysis with promising results. Lifelong learning aims to imitate human learning by learning continuously, retaining/accumulating the knowledge learned in the past, and using or transferring the past knowledge to help new task learning and problem solving. In the process, the learner becomes more and more knowledgeable and better and better at learning. Traditional machine learning learns in isolation, and it uses only the data of the particular application to learn a model. This research will design more effective principled lifelong learning algorithms for sentiment analysis and to create a continually updating knowledge base of attributes (or aspects), e.g., for electronic products, and opinions about them and develop a single framework to significantly boost the performance of sentiment analysis. To broaden the impacts of this project, mature technologies will be transferred to industry and demonstrated and used in classes, and students will participate in the research. Specifically, the project will develop novel lifelong learning algorithms for four core sub-problems of sentiment analysis with the goal to improve: (1) aspect extraction through learning on the job, which learns to improve the model while working after model building; (2) aspect grouping through lifelong aspect topic modeling, which uses past grouping of aspects to help new grouping; (3) aspect sentiment classification using lifelong attention models, which retain the previous attention distributions and leverage them to build more accurate sentiment classifiers for new tasks; and (4) coreference resolution via continuous association learning to discover aspect and sentiment related coreference relations. The resulting algorithms will be incorporated into a holistic lifelong heterogeneous-task learning model to significantly improve sentiment analysis accuracy. Beyond sentiment analysis, the project will also develop general lifelong learning algorithms that can be applied to a wide range of other applications based on insights gained from lifelong sentiment analysis. For dissemination, in addition to publishing research papers, we will organize workshops and give conference tutorials on the project topic, and release our annotated data and implemented software to the research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.