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1. Using Topic Modeling for Code Discovery in Large Scale Text Data

   https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_2  

   Cai, Zhiqiang ; Siebert-Evenstone, Amanda ; Eagan, Brendan R. ; Shaffer, David W. ( January 2021 , Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings)

   Ruis, Andrew ; Lee, Seung B. (Ed.)

   When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of “topics” in the data, which researchers then use to interpret meaning of the topics. A topic model also gives each document in the dataset a score for each topic, which can be used as a non-binary coding for what proportion of a topic is in the document. Unfortunately, it is often difficult to interpret what the topics mean in a defensible way, or to validate document topic proportion scores as meaningful codes. In this study, we examine how keywords from codes developed by human experts were distributed in topics generated from topic modeling. The results show that (1) top keywords of a single topic often contain words from multiple human-generated codes; and conversely, (2) words from human-generated codes appear as high-probability keywords in multiple topic. These results explain why directly using topics from topic models as codes is problematic. However, they also imply that topic modeling makes it possible for researchers to discover codes from short word lists. 

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2. MVTS-Data Toolkit: A Python package for preprocessing multivariate time series data

   https://doi.org/10.1016/j.softx.2020.100518  

   Ahmadzadeh, Azim ; Sinha, Kankana ; Aydin, Berkay ; Angryk, Rafal A. ( July 2020 , SoftwareX)

   null (Ed.)
3. CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media

   https://doi.org/10.1177/0081175019860244  

   Zhang, Han ; Pan, Jennifer ( July 2019 , Sociological Methodology)

   Protest event analysis is an important method for the study of collective action and social movements and typically draws on traditional media reports as the data source. We introduce collective action from social media (CASM)—a system that uses convolutional neural networks on image data and recurrent neural networks with long short-term memory on text data in a two-stage classifier to identify social media posts about offline collective action. We implement CASM on Chinese social media data and identify more than 100,000 collective action events from 2010 to 2017 (CASM-China). We evaluate the performance of CASM through cross-validation, out-of-sample validation, and comparisons with other protest data sets. We assess the effect of online censorship and find it does not substantially limit our identification of events. Compared to other protest data sets, CASM-China identifies relatively more rural, land-related protests and relatively few collective action events related to ethnic and religious conflict. 

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4. Comparative text analytics via topic modeling in banking

   https://doi.org/10.1109/SSCI.2017.8280945  

   Chen, Yu ; Rabbani, Rhaad M. ; Gupta, Aparna ; Zaki, Mohammed J. ( November 2017 , 2017 IEEE Symposium Series on Computational Intelligence -- CIFER)
5. Text Mining of CHO Cell Bibliome: Topic Modeling and Document Classification

   Wang, Qinghua ; Olshin, Jonathan ; Vijay-Shanker, K. ; Wu, Cathy ( August 2022 , bioRxiv)

   Chinese hamster ovary (CHO) cells are widely used for mass production of therapeutic proteins in the pharmaceutical industry. With the growing need in optimizing the performance of producer CHO cell lines, research on CHO cell line development and bioprocess continues to increase in recent decades. Bibliographic mapping and classification of relevant research studies will be essential for identifying research gaps and trends in literature. To qualitatively and quantitatively understand the CHO literature, we have conducted topic modeling using a CHO bioprocess bibliome manually compiled in 2016, and compared the topics uncovered by the Latent Dirichlet Allocation (LDA) models with the human labels of the CHO bibliome. The results show a significant overlap between the manually selected categories and computationally generated topics, and reveal the machine-generated topic-specific characteristics. To identify relevant CHO bioprocessing papers from new scientific literature, we have developed a supervised learning model, Logistic Regression, to identify specific article topics and evaluated the results using three CHO bibliome datasets, Bioprocessing set, Glycosylation set, and Phenotype set. The use of top terms as features supports the explainability of document classification results to yield insights on new CHO bioprocessing papers. 

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