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.
