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Updated: Sep 4, 2025
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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A social and news media benchmark dataset for topic modeling
Samuel Miles1, Lixia Yao2, Weilin Meng2
1Department of Electrical and Computer Engineering, IUPUI, Indianapolis, IN 46202, USA.
Data in Brief
|July 21, 2022
Summary
This study introduces two new datasets for topic modeling research, enabling direct comparison of techniques like pPSO, ETM, and NVDM. These datasets facilitate reproducible research in natural language processing and text analysis.
Area of Science:
- Natural Language Processing
- Machine Learning
- Data Science
Background:
- Topic modeling research faces challenges in comparing techniques due to unavailable data and preprocessing steps.
- Recent advancements focus on vector embeddings with generative and evolutionary topic modeling.
Discussion:
- Presents two secondary datasets derived from a cancer health forum and newsgroups.
- Details preprocessing steps (punctuation, stop word, high frequency word removal).
- Datasets support comparison of pPSO, ETM, and NVDM topic modeling techniques with varying topic numbers (10, 20, 30) and embeddings (sBERT, Skipgram).
Key Insights:
- The datasets enable direct, reproducible comparisons of diverse topic modeling approaches.
- Includes unique identifiers for original documents, preprocessing scripts, and generated topic keywords.
- Provides the algorithm for the pPSO evolutionary topic modeling technique.
Outlook:
- Facilitates standardized evaluation of topic modeling algorithms.
- Encourages further research and development in comparative text analysis.
- Aims to advance the field of unsupervised text representation and understanding.
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