Mining correlated bursty topic patterns from coordinated text streams

2007 ◽  
Author(s):  
Xuanhui Wang ◽  
ChengXiang Zhai ◽  
Xiao Hu ◽  
Richard Sproat
Keyword(s):  
Text Streams

Mining Hotspots from Multiple Text Streams Based on Stream Information Distance

2011 ◽  
Vol 22 (8) ◽  
pp. 1761-1770 ◽  
Author(s):  
Ning YANG ◽  
Chang-Jie TANG ◽  
Yue WANG ◽  
Yu CHEN ◽  
Jiao-Ling ZHENG ◽  
...  
Keyword(s):  
Information Distance ◽  
Text Streams

Efficient clustering of short text streams using online-offline clustering

2021 ◽  
Author(s):  
Md Rashadul Hasan Rakib ◽  
Norbert Zeh ◽  
Evangelos Milios
Keyword(s):  
Short Text ◽  
Text Streams

Classifying High-Speed Text Streams

2003 ◽  
pp. 148-160 ◽  
Author(s):  
Gabriel Pui Cheong Fung ◽  
Jeffrey Xu Yu ◽  
Hongjun Lu
Keyword(s):  
High Speed ◽  
Text Streams

scholarly journals Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7115
Author(s):  
Amin Muhammad Sadiq ◽  
Huynsik Ahn ◽  
Young Bok Choi
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Large Scale ◽  
Activity Analysis ◽  
Physical Activities ◽  
Future Research ◽  
Crowd Sourcing ◽  
Emergency Relief ◽  
The Public ◽  
Text Streams

A rapidly increasing growth of social networks and the propensity of users to communicate their physical activities, thoughts, expressions, and viewpoints in text, visual, and audio material have opened up new possibilities and opportunities in sentiment and activity analysis. Although sentiment and activity analysis of text streams has been extensively studied in the literature, it is relatively recent yet challenging to evaluate sentiment and physical activities together from visuals such as photographs and videos. This paper emphasizes human sentiment in a socially crucial field, namely social media disaster/catastrophe analysis, with associated physical activity analysis. We suggest multi-tagging sentiment and associated activity analyzer fused with a a deep human count tracker, a pragmatic technique for multiple object tracking, and count in occluded circumstances with a reduced number of identity switches in disaster-related videos and images. A crowd-sourcing study has been conducted to analyze and annotate human activity and sentiments towards natural disasters and related images in social networks. The crowdsourcing study outcome into a large-scale benchmark dataset with three annotations sets each resolves distinct tasks. The presented analysis and dataset will anchor a baseline for future research in the domain. We believe that the proposed system will contribute to more viable communities by benefiting different stakeholders, such as news broadcasters, emergency relief organizations, and the public in general.

Generation of topic evolution graphs from short text streams

2020 ◽  
Vol 383 ◽  
pp. 282-294 ◽  
Author(s):  
Wang Gao ◽  
Min Peng ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Weiguang Han ◽  
...  
Keyword(s):  
Short Text ◽  
Topic Evolution ◽  
Text Streams

scholarly journals Efficient classification of multi-labeled text streams by clashing

2014 ◽  
Vol 41 (11) ◽  
pp. 5431-5450 ◽  
Author(s):  
Ricardo Ñanculef ◽  
Ilias Flaounas ◽  
Nello Cristianini
Keyword(s):  
Text Streams

Persistent queries over dynamic text streams

2005 ◽  
Vol 3 (3/4) ◽  
pp. 288
Author(s):  
Javed Aslam ◽  
Ekaterina Pelekhov ◽  
Daniela Rus
Keyword(s):  
Text Streams
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