LEADER 04022nam 22006255 450 001 9911015964903321 005 20250722130302.0 010 $a981-9688-53-1 024 7 $a10.1007/978-981-96-8853-1 035 $a(CKB)39698594900041 035 $a(MiAaPQ)EBC32227303 035 $a(Au-PeEL)EBL32227303 035 $a(DE-He213)978-981-96-8853-1 035 $a(EXLCZ)9939698594900041 100 $a20250722d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTopic Modeling $eAdvanced Techniques and Applications /$fby Yanghui Rao, Qing Li 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (291 pages) 225 1 $aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 311 08$a981-9688-52-3 327 $aChapter 1. Introduction -- Chapter 2. Classical Topic Models -- Chapter 3. Modern Topic Models -- Chapter 4. Applications -- Chapter 5. Discussions. 330 $aAs a well-known text mining tool, topic modeling can effectively discover the latent semantic structure of text data. Extracting topics from documents is also one of the fundamental challenges in natural language processing. Although topic models have seen significant achievements over the past three decades, there remains a scarcity of methods that effectively model temporal aspect. Moreover, many contemporary topic models continue to grapple with the issue of noise contamination, particularly in social media data. This book presents several approaches designed to address these two limitations. Initially, traditional lifelong topic models aim to accumulate knowledge learned from experience for future task. However, the sequence of topics extracted by these methods may shift over time, leading to semantic misalignment between the topic representations across document streams. Such misalignment can degrade the performances of various downstream tasks, including online document classification and dynamic information retrieval at the topic level. Additionally, the challenge of coherent topic modeling is particularly relevant due to the noise and large scale of social media datasets. Messages on social media platforms often consists of only a few words, resulting in a lack of significant context. Models applied directly to this type of text frequently encounter the problem of feature sparsity, which can yield unsatisfactory outcomes. In the context of emotion detection, public emotions are known to fluctuate across different topics, and topics can evoke public emotion. Thus, there is a strong interconnection between topic discovery and emotion detection. Jointly modeling topics and emotions is a suitable strategy for these tasks. This book also examines the impact of topics on emotion detection and other related areas. 410 0$aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 606 $aNatural language processing (Computer science) 606 $aMachine learning 606 $aArtificial intelligence$xData processing 606 $aComputational linguistics 606 $aNatural Language Processing (NLP) 606 $aMachine Learning 606 $aData Science 606 $aComputational Linguistics 615 0$aNatural language processing (Computer science) 615 0$aMachine learning. 615 0$aArtificial intelligence$xData processing. 615 0$aComputational linguistics. 615 14$aNatural Language Processing (NLP). 615 24$aMachine Learning. 615 24$aData Science. 615 24$aComputational Linguistics. 676 $a006.35 700 $aRao$b Yanghui$01834967 701 $aLi$b Qing$0431258 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911015964903321 996 $aTopic Modeling$94410650 997 $aUNINA