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Researchers using social media data want to understand the discussions occurring in and about their respective fields. These domain experts often turn to topic models to help them see the entire landscape of the conversation, but unsupervised topic models often produce topic sets that miss topics experts expect or want to see. To solve this problem, we propose Guided Topic-Noise Model (GTM), a semi-supervised topic model designed with large domain-specific social media data sets in mind. The input to GTM is a set of topics that are of interest to the user and a small number of words or phrases that belong to those topics. These seed topics are used to guide the topic generation process, and can be augmented interactively, expanding the seed word list as the model provides new relevant words for different topics. GTM uses a novel initialization and a new sampling algorithm called Generalized Polya Urn (GPU) seed word sampling to produce a topic set that includes expanded seed topics, as well as new unsupervised topics. We demonstrate the robustness of GTM on open-ended responses from a public opinion survey and four domain-specific Twitter data sets.
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Measuring constitutional preferences: A new method for analyzing public consultation data
Public consultation has become an indispensable part of constitutional design, yet the voluminous, narrative data produced are often impractical to analyze. There are also few, if any, standards for such analysis. Using a comprehensive reference ontology from the Comparative Constitutions Project (CCP), we develop a new methodology to identify constitutional topics of most concern to citizens and compare these to topics in constitutions globally. We analyze data from Chile’s 2016 public consultations—an ambitious process that produced nearly 265,000 narrative responses and launched the constitutional reform process that remains underway today. We leverage advances in natural language processing, in particular sentence-level semantic similarity technology, to classify consultation responses with respect to constitutional topics. Our methodology has potential for advocates, drafters, and researchers seeking to analyze public consultation data that too often go unexamined.
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- Award ID(s):
- 2315189
- PAR ID:
- 10527552
- Editor(s):
- Gutmann, Jerg
- Publisher / Repository:
- PLOS ONE
- Date Published:
- Journal Name:
- PLOS ONE
- Volume:
- 18
- Issue:
- 12
- ISSN:
- 1932-6203
- Page Range / eLocation ID:
- e0295396
- Subject(s) / Keyword(s):
- Constitutional design public consultation conceptual ecology concept alignment semantic similarity Chile Latin America
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pone.0295396
