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An event extraction model based on timeline and user analysis in Latent Dirichlet allocation
2014
Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR '14
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The Latent Dirichlet Allocation (LDA) topic model is adapted with the weights of event terms on timeline and reliable users to extract social events. ...
This paper proposes an event extraction method which combines user reliability and timeline analysis. ...
This paper proposes an event extraction model, which is a Latent Dirichlet Allocation topic model based on timeline and user behavior analysis. ...
doi:10.1145/2600428.2609541
dblp:conf/sigir/TsolmonL14
fatcat:5azgsjqi4bgwdcxgbtwvh3ac7q
Event-based text visual analytics
2014
2014 IEEE Conference on Visual Analytics Science and Technology (VAST)
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In addition to applying entity extraction and topic modeling, we enable the user to explore a large dataset using multi-model semantic interaction, which infers analytical reasoning from user actions to ...
We present an event-based approach for solving a directed sensemaking task in which we combine powerful information foraging tools with intuitive synthesis spaces to solve the VAST 2014 Mini-Challenge ...
ACKNOWLEDGMENTS This work was supported in part by NSF grants IIS-1218346 and SES-1111239. ...
doi:10.1109/vast.2014.7042552
dblp:conf/ieeevast/WangBN14
fatcat:j3gfrdojyvbt7pqudg5bmo5nay
Visualization of Clandestine Labs from Seizure Reports: Thematic Mapping and Data Mining Research Directions
[article]
2015
arXiv
pre-print
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We present an approach to event extraction that is driven by data mining and visualization goals, particularly thematic mapping and trend analysis. ...
The problem of spatiotemporal event visualization based on reports entails subtasks ranging from named entity recognition to relationship extraction and mapping of events. ...
Figure 3 . 3 Plate model for Latent Dirichlet Allocation (LDA) in a system with an N-word lexicon, D documents, and K topics. ...
arXiv:1503.01549v1
fatcat:5usymj3c6zhn7jsf426s2mic6a
Analysis of Topic Modeling with Unpooled and Pooled Tweets and Exploration of Trends during Covid
2021
International Journal of Computer Science Engineering and Applications
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Topic modeling is an unsupervised algorithm to discover a hidden pattern in text documents. In this study, we explore the Latent Dirichlet Allocation (LDA) topic model algorithm. ...
Social media analytics helps make informed decisions based on people's needs and opinions. ...
Focus on analyzing the topics and themes during this period on Twitter using Latent Dirichlet Allocation (LDA) topic modeling approach. ...
doi:10.5121/ijcsea.2021.11601
fatcat:skusdyt5uneifpobgsehvrurai
RescueMark: Visual Analytics of Social Media Data for Guiding Emergency Response in Disaster Situations: Award for Skillful Integration of Language Model
2019
2019 IEEE Conference on Visual Analytics Science and Technology (VAST)
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We describe the data analysis and visualization process of the social media data applied to extract the relevant information. ...
RescueMark provides spatial, topic and temporal event exploration supporting decision making for resource allocation and determine damaged areas of the city. ...
For this, we applied the Latent Dirichlet Allocation (LDA) algorithm [1] to extract topic terms for each messages. ...
doi:10.1109/vast47406.2019.8986898
dblp:conf/ieeevast/JeitlerTMJBSK19
fatcat:xtkldccevvbathh4hh42kfjme4
User Group Oriented Temporal Dynamics Exploration
2014
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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This paper proposes GrosToT (Group Specific Topics-over-Time), a unified probabilistic model to infer latent user groups and temporal topics at the same time. ...
Specifically, groups' attention to their medium-interested topics are event-driven, showing rich bursts; while its engagement in group's dominating topics are interest-driven, remaining stable over time ...
Acknowledgments This research is supported by the National Natural Science Foundation of China under Grant No. 61272155, and 973 program under No. 2014CB340405. ...
doi:10.1609/aaai.v28i1.8723
fatcat:qrnkx7sqqnemfnwdcpxyutwriy
Hashtags are (not) judgemental: The untold story of Lok Sabha elections 2019
[article]
2020
arXiv
pre-print
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Second, we use Latent Dirichlet Allocation to find topic patterns in the dataset. In the end, we use skip-gram word embedding model to find semantically similar hashtags. ...
We study the trends and events unfolded on the ground, the latent topics to uncover representative hashtags and semantic similarity to relate hashtags with the election outcomes. ...
We used Latent Dirichlet Allocation (LDA) to find latent topics among the hashtags in an unsupervised manner. ...
arXiv:1909.07151v2
fatcat:ahisl3a2rzcivbm2v7lurpaite
Time and information retrieval: Introduction to the special issue
2015
Information Processing & Management
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using Latent Dirichlet Allocation Model" (Kar, Nunes, & Ribeiro, 2015) . ...
Evaluation over a set of Wikipedia articles shows that a method based on Latent Dirichlet Allocation achieved strong above-baseline performance. ...
doi:10.1016/j.ipm.2015.05.002
fatcat:rgh7qcsn3zfztb637r3zcqsjkm
A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization
2016
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Previous studies in extractive timeline generation are limited in two ways: first, most prior work focuses on fully-observable ranking models or clustering models with hand-designed features that may not ...
In experiments, we compare our model to various competitive baselines, and demonstrate the stateof-the-art performance of the proposed textbased and multimodal approaches. ...
Acknowledgments The authors would like to thank Kapil Thadani and the anonymous reviewers for their thoughtful comments. ...
doi:10.18653/v1/n16-1008
dblp:conf/naacl/WangMRS16
fatcat:iv2x23umuralnbt7oask3xs2yy
Building a semantic recommendation engine for news feeds based on emerging topics from tweets
2016
2016 15th RoEduNet Conference: Networking in Education and Research
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In this paper, we approach the problem of topic extraction from Twitter in the context of designing a recommendation engine to best matching user profiles to news feed articles. ...
Alongside its advent, textual analysis changed as new user-centered content failed to comply with traditional grammar ruling. ...
Acknowledgment The work presented in this paper was partially funded by the EC H2020 project RAGE (Realising and Applied Gaming Eco-System) http://www.rageproject.eu/ Grant agreement No 644187. ...
doi:10.1109/roedunet.2016.7753209
fatcat:fywcfbdd3bdkznzhsvdiyxih3y
Social-media insights into US mental health amid the COVID-19 global pandemic: a Longitudinal analysis of publicly available Twitter data (January 22- April 10, 2020) (Preprint)
2020
Journal of Medical Internet Research
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First, we characterized the evolution of hashtags over time using Latent Dirichlet Allocation (LDA) topic modeling. ...
Finally, VADER sentiment analysis sentiment scores of user timelines were initially high and stable, but decreased significantly, and continuously, by late March. ...
Latent Dirichlet Allocation Topic Models Latent Dirichlet allocation (LDA) topic models are unsupervised machine learning tools that perform probabilistic inferences to consolidate large volumes of text ...
doi:10.2196/21418
pmid:33284783
fatcat:ryq3uhkmqbazlbp5nqhdctwlga
A Semantic Aspect-Based Vector Space Model to Identify the Event Evolution Relationship within Topics
2015
Journal of Computing Science and Engineering
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A topic usually consists of multiple related events, and the accurate identification of event evolution relationship plays an important role in topic evolution analysis. ...
Then, construct a semantic aspect-based vector space model according to these aspects. Finally, represent each event as a point and measure the semantic relatedness between events in the space. ...
In addition, the topic model can be less affected by synonymy and polysemy. Latent Dirichlet allocation (LDA) [11] is a typical topic model. ...
doi:10.5626/jcse.2015.9.2.73
fatcat:nv3tednaxvbqhc5mb5qitqezni
Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning
2018
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18
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We exploit rich spatio-temporal representations on user activity at cultural venues and use a novel extended version of the traditional latent Dirichlet allocation model that incorporates temporal information ...
To this end, the optimal allocation of cultural establishments and related resources across urban regions becomes of vital importance, as it can reduce financial costs in terms of planning and improve ...
We would also like to thank Bo Chen and Huanzhong Duan in the WeChat group for their valuable comments and helpful suggestions. ...
doi:10.1145/3219819.3219929
dblp:conf/kdd/ZhouNMZ18
fatcat:dicu37s4znbifbyumxat6ilgsa
Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition
2012
2012 IEEE Conference on Visual Analytics Science and Technology (VAST)
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In order to find and understand abnormal events, the analyst can first extract major topics from a set of se- lected messages and rank them probabilistically using Latent Dirichlet Allocation. ...
In analyzing social media data, researchers have mainly focused on finding temporal trends according to volume-based importance. ...
Department of Homeland Securitys VACCINE Center under Award Number 2009-ST-061-CI0003, the German Federal Ministry for Education and Research (BMBF) as part of the VASA project, and the European Commission ...
doi:10.1109/vast.2012.6400557
dblp:conf/ieeevast/ChaeTBJMEE12
fatcat:gevduox26fdddipfh5go3dqexa
Context-aware social media user sentiment analysis
2020
Tsinghua Science and Technology
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In this study, we propose a novel model for performing context-aware user sentiment analysis. ...
Based on our experimental results obtained using the Twitter dataset, our approach is observed to outperform the other existing methods in analysing user sentiment. ...
Fig. 8 8 Performance comparison based on the contributions of comments and users' timelines. ...
doi:10.26599/tst.2019.9010021
fatcat:ib6ezkm4onbu5cuqcgqwflml4a
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