DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework

The emergence of social media platforms like Twitter has become a prominent communication source in disaster outbreak. NGOs, Government agencies leverage twitter’s open and public features to provide immediate relief. Nevertheless, situational information gets immersed in millions of twee...

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Autores principales:	Diksha Bansal, Naveen Saini, Sriparna Saha
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Evolving tweet-stream summarization classification convolution neural network clustering multi-objective optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3622835603084d94b77e17a0182c1940
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id	oai:doaj.org-article:3622835603084d94b77e17a0182c1940
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spelling	oai:doaj.org-article:3622835603084d94b77e17a0182c19402021-11-18T00:07:53ZDCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework2169-353610.1109/ACCESS.2021.3120112https://doaj.org/article/3622835603084d94b77e17a0182c19402021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9570355/https://doaj.org/toc/2169-3536The emergence of social media platforms like Twitter has become a prominent communication source in disaster outbreak. NGOs, Government agencies leverage twitter’s open and public features to provide immediate relief. Nevertheless, situational information gets immersed in millions of tweets with varying characteristics. Examining each tweet can be cumbersome and time-consuming. Thus, the efficient extraction of disaster-related situational tweets and getting information from all the extracted tweets is required. In the current paper, we have developed a novel framework that uses a deep learning-based classification model to separate the situational tweets from others and summarize them in real-time. Our system is a three-phase process: (a) Creating tweet clusters using a representative set of tweets from the initial set of extracted tweets using a multi-objective optimization concept; (b) When a new tweet arrives, the clusters are updated. The new tweet is classified as situational vs. non-situational. If situational, it is assigned to the closest cluster or new cluster. This assignment is based on its weighted average of syntactic and semantic distances and relevancy to the cluster; (c) Summary is formulated by extracting tweets from each cluster. The proposed approach’s superior performance on four datasets related to different disaster-related events indicates the developed framework’s efficiency over the state-of-the-art techniques.Diksha BansalNaveen SainiSriparna SahaIEEEarticleEvolving tweet-streamsummarizationclassificationconvolution neural networkclusteringmulti-objective optimizationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148325-148338 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Evolving tweet-stream summarization classification convolution neural network clustering multi-objective optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Evolving tweet-stream summarization classification convolution neural network clustering multi-objective optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971 Diksha Bansal Naveen Saini Sriparna Saha DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework
description	The emergence of social media platforms like Twitter has become a prominent communication source in disaster outbreak. NGOs, Government agencies leverage twitter’s open and public features to provide immediate relief. Nevertheless, situational information gets immersed in millions of tweets with varying characteristics. Examining each tweet can be cumbersome and time-consuming. Thus, the efficient extraction of disaster-related situational tweets and getting information from all the extracted tweets is required. In the current paper, we have developed a novel framework that uses a deep learning-based classification model to separate the situational tweets from others and summarize them in real-time. Our system is a three-phase process: (a) Creating tweet clusters using a representative set of tweets from the initial set of extracted tweets using a multi-objective optimization concept; (b) When a new tweet arrives, the clusters are updated. The new tweet is classified as situational vs. non-situational. If situational, it is assigned to the closest cluster or new cluster. This assignment is based on its weighted average of syntactic and semantic distances and relevancy to the cluster; (c) Summary is formulated by extracting tweets from each cluster. The proposed approach’s superior performance on four datasets related to different disaster-related events indicates the developed framework’s efficiency over the state-of-the-art techniques.
format	article
author	Diksha Bansal Naveen Saini Sriparna Saha
author_facet	Diksha Bansal Naveen Saini Sriparna Saha
author_sort	Diksha Bansal
title	DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework
title_short	DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework
title_full	DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework
title_fullStr	DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework
title_full_unstemmed	DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework
title_sort	dcbrts: a classification-summarization approach for evolving tweet streams in multiobjective optimization framework
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/3622835603084d94b77e17a0182c1940
work_keys_str_mv	AT dikshabansal dcbrtsaclassificationsummarizationapproachforevolvingtweetstreamsinmultiobjectiveoptimizationframework AT naveensaini dcbrtsaclassificationsummarizationapproachforevolvingtweetstreamsinmultiobjectiveoptimizationframework AT sriparnasaha dcbrtsaclassificationsummarizationapproachforevolvingtweetstreamsinmultiobjectiveoptimizationframework
_version_	1718425216996081664

DCBRTS: A Classification-Summarization Approach for Evolving Tweet Streams in Multiobjective Optimization Framework

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