III: Small: A Holistic Approach to Sentiment Analysis

情感分析是提取和分类人的任务吗？S在社交媒体和其他来源表达的情绪、观点和情感。它有非常广泛的应用，因为消费者和公众的意见，情绪表达对企业，组织和个人的决策都有帮助。虽然情感分析在过去得到了广泛的研究，但其准确性仍然较低，限制了其应用范围。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，来帮助解决情感分析的一些子问题，并取得了有希望的结果。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，并利用或转移过去的知识来帮助学习新的任务和解决问题。在这个过程中，学习者变得越来越博学，越来越擅长学习。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。本研究将为情感分析设计更有效的原则终身学习算法，并创建一个持续更新的属性（或方面）知识库，例如电子产品，以及关于它们的意见，并开发一个单一的框架来显着提高情感分析的性能。为了扩大这个项目的影响，成熟的技术将被转移到工业，并在课堂上演示和使用，学生将参与研究。具体而言，该项目将针对情感分析的四个核心子问题开发新的终身学习算法，其目标是：(1)通过在工作中学习的方面提取，在模型建立后的工作中学习改进模型；(2)通过终身方面主题建模进行方面分组，利用过去的方面分组来帮助新的方面分组；(3)使用终身注意模型进行面向情感分类，该模型保留了以前的注意力分布，并利用它们为新的任务构建更准确的情感分类器；(4)通过持续的关联学习来发现与方面和情感相关的共指关系。所得到的算法将被纳入一个整体的终身异构任务学习模型，以显着提高情感分析的准确性。除了情感分析，该项目还将开发通用终身学习算法，该算法可以基于终身情感分析获得的见解应用于广泛的其他应用。为了传播，除了发表研究论文外，我们还将组织关于项目主题的研讨会和会议教程，并向研究社区发布我们注释的数据和实现的软件。该奖项反映了美国国家科学基金会的法定使命，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.