Normalized Approach to Find Optimal Number of Topics in Latent Dirichlet Allocation (LDA)

Latent dirichlet allocation

(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337 · see citing works

Exploring the Space of Topic Coherence Measures

Röder, M., Both, A., & Hinneburg, A. (2015) · Web Search and Data Mining · doi:10.1145/2684822.2685324 · Cited by 2,501 · see citing works

Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X.-H., Li, Y., & Zhao, L. (2017) · Multimedia tools and applications · doi:10.1007/s11042-018-6894-4 · Cited by 1,875 · see citing works

Computational Linguistics and Intelligent Text Processing

Gelbukh, A. F. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45715-1 · Cited by 836 · see citing works

Smart literature review: a practical topic modelling approach to exploratory literature review

Asmussen, C. B., & Møller, C. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0255-7 · Cited by 467 · see citing works

A heuristic approach to determine an appropriate number of topics in topic modeling

Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., & Zou, W. (2015) · BMC Bioinformatics · doi:10.1186/1471-2105-16-s13-s8 · Cited by 381 · see citing works

A network approach to topic models

Gerlach, M., Peixoto, T. P., & Altmann, E. G. (2018) · Science Advances · doi:10.1126/sciadv.aaq1360 · Cited by 245 · see citing works

Topic modeling, long texts and the best number of topics. Some Problems and solutions

Sbalchiero, S., & Eder, M. (2020) · Quality & Quantity · doi:10.1007/s11135-020-00976-w · Cited by 121 · see citing works

Examining the Coherence of the Top Ranked Tweet Topics

Fang, A., Macdonald, C., Ounis, I., et al. (2016) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2911451.2914731 · Cited by 24 · see citing works

Optimization of Topic Recognition Model for News Texts Based on LDA

Wang, H., Wang, J., Zhang, Y., et al. (2019) · Journal of Digital Information Management · doi:10.6025/jdim/2019/17/5/257-269 · Cited by 21 · see citing works

Perplexity on Reduced Corpora

Kobayashi, H. (2014) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/v1/p14-1075 · Cited by 15 · see citing works

A modified multi objective heuristic for effective feature selection in text classification

Thiyagarajan, D., & Shanthi, N. (2017) · Cluster Computing · doi:10.1007/s10586-017-1150-7 · Cited by 13 · see citing works

Simulated Annealing Based Algorithm for Tuning LDA Hyper Parameters

Pathik, N., & Shukla, P. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-4032-5_47 · Cited by 11 · see citing works

Optimising topic coherence with Weighted P o´ lya Urn scheme

Wang, R., Zhou, D., & He, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.12.013 · Cited by 9 · see citing works

Reliability of Perplexity to Find Number of Latent Topics.

Neishabouri, A., & Desmarais, M. C. (2020) · PolyPublie (École Polytechnique de Montréal) · Cited by 9 · see citing works

Improving Topic Coherence Using Parsimonious Language Model and Latent Semantic Indexing

Dewangan, J. K., Sharaff, A., & Pandey, S. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-1420-3_89 · Cited by 7 · see citing works

Incorporating Prior Knowledge by Selective Context Features to Enhance Topic Coherence

Huang, C. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9190-3_32 · Cited by 5 · see citing works

A Hybrid HDP-ME-LDA Model for Sentiment Analysis

Yuan, B., & Wu, G. (2017) · International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE) · doi:10.2991/amcce-17.2017.114 · Cited by 4 · see citing works

Experiment report of TREC 2005 Genomics track ad hoc retrieval task

Zhou, W., & Yu, C. T. (2005) · Text REtrieval Conference · Cited by 3 · see citing works

Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues

Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works

The Nexus Between Quality of Customer Relationship Management Systems and Customers’ Satisfaction: Evidence from Online Customers’ Reviews

Nilashi, M., Abumalloh, R. A., Ahmadi, H., Samad, S., Alrizq, M., Abosaq, H., & Alghamdi, A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21828 · Cited by 30 · see citing works

Automatic Topic Clustering Using Latent Dirichlet Allocation with Skip-Gram Model on Final Project Abstracts

Bunyamin, H., & Sulistiani, L. (2017) · International Computer Science and Engineering Conference · doi:10.1109/icsec.2017.8443795 · Cited by 2 · see citing works

Estimating Word Probabilities with Neural Networks in Latent Dirichlet Allocation

Masada, T. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67274-8_12 · Cited by 1 · see citing works

Topic Modeling on News Articles using Latent Dirichlet Allocation

Kretinin, M., & Nguyen, G. (2022) · IEEE International Conference on Intelligent Engineering Systems (INES) · doi:10.1109/ines56734.2022.9922609 · Cited by 10 · see citing works

Topic Modeling on WhatsApp User Reviews Using Latent Dirichlet Allocation

Kharisudin, I., & Masri'an, H. (2022) · Scientific Journal of Informatics · doi:10.15294/sji.v9i1.34941 · Cited by 3 · see citing works

Analisis Topik Modelling Terhadap Penggunaan Sosial Media Twitter oleh Pejabat Negara

Patmawati, P., & Yusuf, M. (2021) · Building of Informatics Technology and Science (BITS) · doi:10.47065/bits.v3i3.1012 · Cited by 14 · see citing works