Work

Enabling Semantic Topic Modeling on Twitter Using MetaMap.

Year: 2024

Type: article

Abstract: Topic modeling performs poorly on short phrases or sentences and ever-changing slang, which are common in social media, such as X, formerly known as Twitter. This study investigates whether concept annotation tools such as MetaMap can enable topic modeling at the semantic level. Using tweets mentioning "hydroxychloroquine" for a case study, we extracted 56,017 posted between 03/01/2020-12/31/2021. The tweets were run through MetaMap to encode concepts with UMLS Concept Unique Identifiers (CUIs) and then we used Latent Dirichlet Allocation (LDA) to identify the optimal model for two datasets: 1) tweets with the original text and 2) tweets with the replaced CUIs. We found that the MetaMap LDA models outperformed the non-MetaMap models in terms of coherence and representativeness and identified topics timely relevant to social and political discussions. We concluded that integrating MetaMap to standardize tweets through UMLS concepts improved semantic topic modeling performance amidst noise in the text.

Source: PubMed Central

Authors: Rebecca Shyu, Chunhua Weng

Institution: Columbia University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Georgia Clinical and Translational Science Alliance, National Institutes of Health, U.S. National Library of Medicine, National Center for Advancing Translational Sciences

Awards: Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical Data, Clinical and Translational Science Award, ClinEX - Clinical Evidence Extraction, Representation, and Appraisal, Training in Biomedical Informatics at Columbia University

OpenAlex ID: W4399317295