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Found 7 papers, 0 papers with code
A comprehensive study on Frequent Pattern Mining and Clustering categories for topic detection in Persian text stream
Frequent pattern and clustering are selected for this research, and a hybrid of both is proposed as a new category.
A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets
Then, all the different combinations of embedding methods with distance metrics and clustering methods are investigated by silhouette metric.
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects
Based on the given extrinsic and intrinsic analysis results, we note that compared to other theoretical-based techniques, the proposed hierarchical clustering approach can better group the users within the adaptive tree.
Cognitive-aware Short-text Understanding for Inferring Professions
Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications.
EmoDNN: Understanding emotions from short texts through a deep neural network ensemble
The latent knowledge in the emotions and the opinions of the individuals that are manifested via social networks are crucial to numerous applications including social management, dynamical processes, and public security.
SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding
Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors.
TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs
We can use the temporal and textual data of the nodes to compute the edge weights and then generate subgraphs with highly relevant nodes.
