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1. 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.)
2. 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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3. 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)
4. FoTo: Targeted Visual Topic Modeling for Focused Analysis of Short Text

   Kumar, S; Le, T (May 2024, ELRA and ICCL)
5. Keyword expansion techniques for mining social movement data on social media

   https://doi.org/10.1140/epjds/s13688-022-00343-9  

   Bozarth, Lia; Budak, Ceren (May 2022, EPJ Data Science)

   Abstract Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords, but systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate five diverse keyword expansion techniques (or pipelines) on five representative social movements across two distinct activity levels. Our results guide researchers who aim to use social media keyword searches to mine data. For instance, we show that word embedding-based methods significantly outperform other even more complex and newer approaches when movements are in normal activity periods. These methods are also less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random. 

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