Text Document Clustering Using Community Discovery Approach

Anu, Beniwal; Roy, Gourav; Bhavani, S. Durga

doi:10.1007/978-3-030-36987-3_22

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Truica et al demonstrated, using the same implementation of LDA that we adopted, that this approach has proven to be the most effective choice in situations with limited vocabulary sizes, particularly when compared to the TF-IDF method [54]. However, this is not the only method with which to analyze Twitter data based on keywords, as the literature has recognized different options that have been considered in other works [55][56][57]. Likewise, there are also available approaches to clustering documents using embeddings or topic labels [58,59].…”

Section: Discussionmentioning

confidence: 99%

Navigating the Digital Neurolandscape: Analyzing the Social Perception of and Sentiments Regarding Neurological Disorders through Topic Modeling and Unsupervised Research Using Twitter

Domingo-Espiñeira,

Fraile-Martínez,

Garcia-Montero

et al. 2024

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Neurological disorders represent the primary cause of disability and the secondary cause of mortality globally. The incidence and prevalence of the most notable neurological disorders are growing rapidly. Considering their social and public perception by using different platforms like Twitter can have a huge impact on the patients, relatives, caregivers and professionals involved in the multidisciplinary management of neurological disorders. In this study, we collected and analyzed all tweets posted in English or Spanish, between 2007 and 2023, referring to headache disorders, dementia, epilepsy, multiple sclerosis, spinal cord injury or Parkinson’s disease using a search engine that has access to 100% of the publicly available tweets. The aim of our work was to deepen our understanding of the public perception of neurological disorders by addressing three major objectives: (1) analyzing the number and temporal evolution of both English and Spanish tweets discussing the most notable neurological disorders (dementias, Parkinson’s disease, multiple sclerosis, spinal cord injury, epilepsy and headache disorders); (2) determining the main thematic content of the Twitter posts and the interest they generated temporally by using topic modeling; and (3) analyzing the sentiments associated with the different topics that were previously collected. Our results show that dementias were, by far, the most common neurological disorders whose treatment was discussed on Twitter, and that the most discussed topics in the tweets included the impact of neurological diseases on patients and relatives, claims to increase public awareness, social support and research, activities to ameliorate disease development and existent/potential treatments or approaches to neurological disorders, with a significant number of the tweets showing negative emotions like fear, anger and sadness, and some also demonstrating positive emotions like joy. Thus, our study shows that not only is Twitter an important and active platform implicated in the dissemination and normalization of neurological disorders, but also that the number of tweets discussing these different entities is quite inequitable, and that a greater intervention and more accurate dissemination of information by different figures and professionals on social media could help to convey a better understanding of the current state, and to project the future state, of neurological diseases for the general public.

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