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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Paper Title:	Entity-Centric Multimodal Aspect-Opinion Mining in Social Media
Authors Name:	Nasrin Shah , S N Gite
Unique Id:	IJSDR1709043
Published In:	Volume 2 Issue 9, October-2017
Abstract:	This article describes the approach we take to the analysis of social networks, combining the extraction of textual and multimedia opinions (images) and focusing on the recognition of entities and events. We examined a particular use case, which is to help archivists select material to be included in a social media file to preserve community memories, moving towards a structured preservation around semantic categories. The textual approach we consider is a rule-based approach wherein we consider a series of subcomponents, taking into account the problems that are there and inherent in the social media colony such as noisy grammar or misspelled words, oath or other form of speech including sarcasm, and so on. The analysis of multimedia content Complements this work to solve ambiguity and provide other contextual information. We propose two main innovations in this work: first, the new combination of tools to extract information from text and multimedia; And second, the adaptation of NLP tools for the exploration of specific information to the problems of social networks.
Keywords:	Application, knowledge mining, probabilistic topic model,LDA, Dirichlet
Cite Article:	"Entity-Centric Multimodal Aspect-Opinion Mining in Social Media", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.2, Issue 9, page no.259 - 264, October-2017, Available :http://www.ijsdr.org/papers/IJSDR1709043.pdf
Downloads:	000337211
Publication Details:	Published Paper ID: IJSDR1709043 Registration ID:170778 Published In: Volume 2 Issue 9, October-2017 DOI (Digital Object Identifier): Page No: 259 - 264 Publisher: IJSDR \| www.ijsdr.org ISSN Number: 2455-2631

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