As a well-known text mining tool, topic modeling can effectively discover the latent semantic structure of text data. Extracting topics from documents is also one of the fundamental challenges in natural language processing. Although topic models have seen significant achievements over the past three decades, there remains a scarcity of methods that effectively model temporal aspect. Moreover, many contemporary topic models continue to grapple with the issue of noise contamination, particularly in social media data.

This book presents several approaches designed to address these two limitations. Initially, traditional lifelong topic models aim to accumulate knowledge learned from experience for future task. However, the sequence of topics extracted by these methods may shift over time, leading to semantic misalignment between the topic representations across document streams. Such misalignment can degrade the performances of various downstream tasks, including online document classification and dynamic information retrieval at the topic level. Additionally, the challenge of coherent topic modeling is particularly relevant due to the noise and large scale of social media datasets. Messages on social media platforms often consists of only a few words, resulting in a lack of significant context. Models applied directly to this type of text frequently encounter the problem of feature sparsity, which can yield unsatisfactory outcomes.

In the context of emotion detection, public emotions are known to fluctuate across different topics, and topics can evoke public emotion. Thus, there is a strong interconnection between topic discovery and emotion detection. Jointly modeling topics and emotions is a suitable strategy for these tasks. This book also examines the impact of topics on emotion detection and other related areas.

ensures semantic stability across document streams for reliable topic evolution analysis overcomes feature sparsity in social media, providing clearer topic insights uncovers emotional responses to topics, enhancing understanding of public sentiment dynamics

Autorentext

Yanghui Rao obtained his bachelor's degree from the Central China Normal University in Wuhan, China; a master's degree from the Graduate University of the Chinese Academy of Science in Beijing, China; and a PhD degree from the City University of Hong Kong in the Hong Kong SAR. He is a currently an associate professor at the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China. Rao's research has been published in prestigious journals and conferences, including IEEE Transactions on Knowledge and Data Engineering , ACM Transactions on Information Systems , IEEE Transactions on Cybernetics , IEEE Transactions on Neural Networks and Learning Systems , ACM Transactions on Knowledge Discovery from Data , ACL , IJCAI , EMNLP , NAACL , and COLING . His research interests lie in natural language processing and text mining, with a focus on topic modeling and unsupervised learning.

Qing Li earned his BEng. degree from Hunan University in Changsha, China, and MSc and PhD degrees in computer science from the University of Southern California in Los Angeles, USA. He is a chair professor at the Hong Kong Polytechnic University, a visiting professor at the Zhejiang University, a guest professor at the University of Science and Technology of China, and an adjunct professor of the Hunan University. Li's research interests encompass multi-modal data modeling, multimedia retrieval and management, and e-learning systems. He has authored over 500 papers in the field and is an active member of the research community, serving as a journal reviewer, programme committee chair/co-chair, and as an organizer/co-organizer of various international conferences. Dr. Li is currently the chairman of the Hong Kong Web Society, a councilor of the Database Society of Chinese Computer Federation, and a steering committee member of the international WISE Society. He is a fellow of IEEE, AAIA, and IET.

Inhalt

Chapter 1. Introduction.- Chapter 2. Classical Topic Models.- Chapter 3. Modern Topic Models.- Chapter 4. Applications.- Chapter 5. Discussions.