Graggle: A Graph-based Approach to Document Clustering

Document recommendation systems have traditionally relied upon high-dimensional vector representations that scale poorly in corpora with diverse vocabularies. Existing graph-based approaches focus on the metadata of documents and, unfortunately, ignore the content of the papers. In this work, we have designed and implemented a new system we call Graggle, which builds a graph to model a corpus. Nodes are papers, and edges represent significant words shared between them. We then leverage modern graph learning techniques to turn this graph into a highly efficient tool for dimensionality reduction. Documents are represented as low-dimensional vector embeddings generated with a graph autoencoder. Our experiments show that this approach outperforms traditional document vector-based and text autoencoding approaches on labeled data. Additionally, we have applied this technique to a repository of unlabeled research documents about the novel coronavirus to demonstrate its effectiveness as a real-world tool. © 2022 IEEE.

An Analysis on the Weibo Topic Detection Based on K-means Algorithm

Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. K-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional K-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified K-means algorithm is proposed, which uses Jaccard distance measure for assigning the most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. The experimental results demonstrate that the proposed K-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data. © 2022 IEEE.

A Multi-Objective Optimization-based Clustering Approach for CORD-19 Scholarly Articles

The pandemic disease COVID-19, originated from the SARS-CoV-2 virus has spread globally. Researchers are working tirelessly on areas including studying the transmission of COVID-19, promoting its identification, designing new vaccines and therapies, and recognizing its socio-economic consequences. This extensive research leads to the exploration of thousands of scientific papers related to biology, chemistry, genetics, health, and economy. Therefore, it is essential to develop an intelligent text mining technique for segregating this rich source of data to perform easy access, information retrieval, and interpretation within minimum time and resources. We propose a multi-objective optimization-based document clustering approach for the CORD-19 (COVID-19 Open Research Dataset) dataset in this paper. Here, a new technique utilizing BioBERT has been proposed, which benefits from the and the document text, rather than only the brief , to perceive a concise understanding of the text to generate clusters with better definitions. The main contributions of the proposed work are two-fold: in the first step, we have used BioBERT to generate the sentence embedding which is further used for the document representation. In the next step, we have developed a multi-objective optimization (MOO) based clustering algorithm for grouping the generated document vector representations. In this MOO-based clustering, we have used Non-dominated Sorting Genetic Algorithm-II and Fuzzy c-means algorithm as the underlying MOO and clustering technique, respectively. This model is evaluated using the Silhouette Score (Silhouette score) and Calinski-Harabasz index (CH index), and the clustering solutions are visualized using word clouds. The clustering results exhibit significant improvements over various other existing clustering models. © 2021 IEEE.

Tele-Mental Health for Reaching Out to Patients in a Time of Pandemic: Provider Survey and Meta-analysis of Patient Satisfaction.

BACKGROUND: The COVID-19 pandemic threatened to impact mental health by disrupting access to care due to physical distance measures and the unexpected pressure on public health services. Tele-mental health was rapidly implemented to deliver health care services. OBJECTIVE: The aims of this study were (1) to present state-of-the-art tele-mental health research, (2) to survey mental health providers about care delivery during the pandemic, and (3) to assess patient satisfaction with tele-mental health. METHODS: Document clustering was applied to map research topics within tele-mental health research. A survey was circulated among mental health providers. Patient satisfaction was investigated through a meta-analysis of studies that compared satisfaction scores between tele-mental health and face-to-face interventions for mental health disorders, retrieved from Web of Knowledge and Scopus. Hedges g was used as the effect size measure, and effect sizes were pooled using a random-effect model. Sources of heterogeneity and bias were examined. RESULTS: Evidence on tele-mental health has been accumulating since 2000, especially regarding service implementation, depressive or anxiety disorders, posttraumatic stress disorder, and special populations. Research was concentrated in a few countries. The survey (n=174 respondents from Italy, n=120 international) confirmed that, after the onset of COVID-19 outbreak, there was a massive shift from face-to-face to tele-mental health delivery of care. However, respondents held skeptical views about tele-mental health and did not feel sufficiently trained and satisfied. Meta-analysis of 29 studies (n=2143) showed that patients would be equally satisfied with tele-mental health as they are with face-to-face interventions (Hedges g=-0.001, 95% CI -0.116 to 0.114, P=.98, Q=43.83, I2=36%, P=.03) if technology-related issues were minimized. CONCLUSIONS: Mental health services equipped with tele-mental health will be better able to cope with public health crises. Both providers and patients need to be actively engaged in digitization, to reshape their reciprocal trust around technological innovations.