The Cultural Mapping and Pattern Analysis (CMAP) Visualization Toolkit: Open Source Text Analysis for Qualitative and Computational Social Science
- URL: http://arxiv.org/abs/2510.16140v1
- Date: Fri, 17 Oct 2025 18:24:47 GMT
- Title: The Cultural Mapping and Pattern Analysis (CMAP) Visualization Toolkit: Open Source Text Analysis for Qualitative and Computational Social Science
- Authors: Corey M. Abramson, Yuhan, Nian,
- Abstract summary: The CMAP visualization toolkit is an open-source suite for analyzing and visualizing text data.<n>The toolkit is designed for scholars integrating pattern analysis, data visualization, and explanation in social science.
- Score: 5.761643793915607
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The CMAP (cultural mapping and pattern analysis) visualization toolkit introduced in this paper is an open-source suite for analyzing and visualizing text data - from qualitative fieldnotes and in-depth interview transcripts to historical documents and web-scaped data like message board posts or blogs. The toolkit is designed for scholars integrating pattern analysis, data visualization, and explanation in qualitative and/or computational social science (CSS). Despite the existence of off-the-shelf commercial qualitative data analysis software, there is a dearth of highly scalable open source options that can work with large data sets, and allow advanced statistical and language modeling. The foundation of the toolkit is a pragmatic approach that aligns research tools with social science project goals- empirical explanation, theory-guided measurement, comparative design, or evidence-based recommendations- guided by the principle that research paradigm and questions should determine methods. Consequently, the CMAP visualization toolkit offers a range of possibilities through the adjustment of relatively small number of parameters, and allows integration with other python tools.
Related papers
- Bridging Tool Dependencies and Domain Knowledge: A Graph-Based Framework for In-Context Planning [18.43901336744982]
We present a framework for exploiting dependencies among tools and documents to enhance exemplar artifact generation.<n>Our method begins by constructing a tool knowledge graph from tool schemas, including descriptions, arguments, and output payloads.<n>In parallel, we derive a complementary knowledge graph from internal documents and SOPs, which is then fused with the tool graph.
arXiv Detail & Related papers (2025-10-28T17:50:15Z) - Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
arXiv Detail & Related papers (2025-10-17T02:33:16Z) - Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation [192.53529928861818]
Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI)<n>However, the costs associated with data annotation and model training remain significant.<n>This survey employs active sampling theory to analyze the generalization error and label complexity associated with learning from low-resource data.
arXiv Detail & Related papers (2025-10-10T03:15:42Z) - A Survey of Small Language Models [104.80308007044634]
Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources.
We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
arXiv Detail & Related papers (2024-10-25T23:52:28Z) - PUB: Plot Understanding Benchmark and Dataset for Evaluating Large Language Models on Synthetic Visual Data Interpretation [2.1184929769291294]
This paper presents a novel synthetic dataset designed to evaluate the proficiency of large language models in interpreting data visualizations.
Our dataset is generated using controlled parameters to ensure comprehensive coverage of potential real-world scenarios.
We employ multimodal text prompts with questions related to visual data in images to benchmark several state-of-the-art models.
arXiv Detail & Related papers (2024-09-04T11:19:17Z) - Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models.
Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions.
We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
arXiv Detail & Related papers (2024-05-25T06:41:23Z) - From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making.<n>Large foundation models, such as large language models, have revolutionized various natural language processing tasks.<n>This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
arXiv Detail & Related papers (2024-03-18T17:57:09Z) - generAItor: Tree-in-the-Loop Text Generation for Language Model
Explainability and Adaptation [28.715001906405362]
Large language models (LLMs) are widely deployed in various downstream tasks, e.g., auto-completion, aided writing, or chat-based text generation.
We tackle this shortcoming by proposing a tree-in-the-loop approach, where a visual representation of the beam search tree is the central component for analyzing, explaining, and adapting the generated outputs.
We present generAItor, a visual analytics technique, augmenting the central beam search tree with various task-specific widgets, providing targeted visualizations and interaction possibilities.
arXiv Detail & Related papers (2024-03-12T13:09:15Z) - Automating the Information Extraction from Semi-Structured Interview
Transcripts [0.0]
This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts.
We present a user-friendly software prototype that enables researchers to efficiently process and visualize the thematic structure of interview data.
arXiv Detail & Related papers (2024-03-07T13:53:03Z) - SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural
Topic Models on Social Media Opinion Mining [0.07538606213726905]
Recent research in opinion mining proposed word embedding-based topic modeling methods.
We show how these methods can be used to display correlated topic models on social media texts using SocialVisTUM.
arXiv Detail & Related papers (2021-10-20T14:04:13Z) - Deep Learning Schema-based Event Extraction: Literature Review and
Current Trends [60.29289298349322]
Event extraction technology based on deep learning has become a research hotspot.
This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
arXiv Detail & Related papers (2021-07-05T16:32:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.