Browsing by Author "Liu, Yulong"

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    Framing Minimum Wage Policy by the Democratic Presidential Administrations: Strategies and Ideologies
    Liu, Yulong (Virginia Tech, 2019-07-02)
    Framing analyses have been among the most popular areas of research for scholars in political communication. Similarly, minimum wage legislation has been a popular topic for researchers in labor economics. However, few studies have used framing analysis to investigate the issue of minimum wage. This exploratory quantitative content analysis coded 45 variables in 236 lengthy press documents spanning 84 years of Democratic presidential administrations. More specifically, this study explored presence of generic frames, stakeholders, and ideological identities employed by Democratic presidential administrations since 1933. Results found that Democratic presidential administrations have been generally consistent in framing minimum wage policy. However, ideological discrepancies in Democratic presidents' actual framing practice were detected: a deepening pro-fairness attitude in specific frames and a growing pro-business empathy in stakeholder presence. The study concluded that framing minimum wage policy has become increasingly expressive: partisan identities transcend ideological positions. Democratic administrations generally maintain a single approach when highlighting minimum wage increase and endorse the Fair Labor Standards Act, albeit using different and even conflicting framing practices over time. To sustain the findings, this study suggests an equivalent study on Republican presidential administrations and their framing of minimum wage policy.
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    Topic Modeling Toolkit
    Lin, Jiayue; Pang, Mingkai; Liu, Yulong (Virginia Tech, 2023-05-08)
    The Topic Modeling Toolkit project began with an existing text mining toolkit and aimed to enhance its functionality by incorporating cutting-edge topic modeling techniques. Specifically, BERTopic, CTM, and LDA were used to extract pertinent topics from a corpus of text documents. The resulting web-based platform provides users with a search engine, a recommendation system, and a usable interface for browsing and exploring these topics. In addition to these enhancements, our team also implemented a text-filtering framework and redesigned the user interface using Tailwind CSS. The final deliverables of the project include a fully functional website, user documentation, and an open-source toolkit that can be used to train machine learning models and support browsing and searching for various text datasets. While the current version of the toolkit includes BERTopic, CTM, and LDA, there is potential for future work to incorporate additional topic modeling methods. It is important to note that while the project originally focused on electronic theses and dissertations (ETDs), the resulting platform can be used to explore and comprehend complex subjects within any corpus of text documents. The topic modeling toolkit is available as an open-source package that users can install and use on their own computers. It is available for use and can be used to support browsing and searching for various text datasets. The intended user group for the platform includes researchers, students, and other users interested in exploring and understanding complex topics within a given corpus of text documents. The resulting topic modeling toolkit offers features that facilitate the exploration and comprehension of intricate topics within text document collections. This tool has the potential to aid researchers, students, and other users in their respective fields.