activity
20182021
most citedLarge-Scale Joint Topic, Sentiment & User Preference Analysis for Online Reviews

3 citations · 6 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL20211 cited

Text to Insight: Accelerating Organic Materials Knowledge Extraction via Deep Learning

Xintong Zhao, Steven Lopez, Semion Saikin +2

Scientific literature is one of the most significant resources for sharing knowledge. Researchers turn to scientific literature as a first step in designing an experiment. Given th…

cs.DL2021

HIVE-4-MAT: Advancing the Ontology Infrastructure for Materials Science

Jane Greenberg, Xintong Zhao, Joseph Adair +2

Introduces HIVE-4-MAT - Helping Interdisciplinary Vocabulary Engineering for Materials Science, an automatic linked data ontology application. Covers contextual background for mate…

cs.SI20201 cited

Neural Stochastic Block Model & Scalable Community-Based Graph Learning

Zheng Chen, Xinli Yu, Yuan Ling +1

This paper proposes a novel scalable community-based neural framework for graph learning. The framework learns the graph topology through the task of community detection and link p…

cs.IR20193 cited

Large-Scale Joint Topic, Sentiment & User Preference Analysis for Online Reviews

Xinli Yu, Zheng Chen, Wei-Shih Yang +2

This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variati…

cs.LG20191 cited

Correlated Anomaly Detection from Large Streaming Data

Zheng Chen, Xinli Yu, Yuan Ling +4

Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection…

cs.LG2018

Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews

Zheng Chen, Yong Zhang, Yue Shang +1

This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product aspects)…