This repository implements a pipeline to store various data of files from a large unstructured dataset. These fields are used for topic modeling (wordclouds, based on low-dimensional versions of ...

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Abstract: Topic modeling is a crucial technique for extracting latent themes from unstructured text data, particularly valuable in analyzing survey responses. However, traditional methods often only ...

1 AI Meteorological Research Division, National Institute of Meteorological Sciences, Jeju, Republic of Korea 2 Artificial Intelligence Meteorological Technology Research Society, Korea Meteorological ...

For topic modeling, posts with <50 tokens were removed, leaving 53.81% (4059/7543) of the posts, which were analyzed using latent Dirichlet allocation with coherence score optimization to identify the ...

We used multiple topic mining approaches, such as latent Dirichlet allocation, nonnegative matrix factorization, and word-embedding methods. Sentiment analysis used TextBlob and Valence Aware ...

ABSTRACT: Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can ...