Memotion Analysis through the Lens of Joint Embedding

Memotion Analysis through the Lens of Joint Embedding

Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2021-12
Hauptverfasser: Gunti, Nethra, Ramamoorthy, Sathyanarayanan, Patwa, Parth, Das, Amitava
Format: Artikel
Sprache:eng
Schlagworte:
Embedding
Modal data
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Gunti, Nethra
Ramamoorthy, Sathyanarayanan
Patwa, Parth
Das, Amitava
description Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be used to spread hate and fake information. That along with its growing ubiquity over several social platforms has caused automatic analysis of memes to become a widespread topic of research. In this paper, we report our initial experiments on Memotion Analysis problem through joint embeddings. Results are marginally yielding SOTA.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2597944498</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2597944498</sourcerecordid><originalsourceid>FETCH-proquest_journals_25979444983</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mQw9U3NzS_JzM9TcMxLzKkszixWKMkoyi9NzwDSqQo-qXnFCvlpCl75mXklCq65SakpKZl56TwMrGmJOcWpvFCam0HZzTXE2UO3oCi_sDS1uCQ-K7-0CGhgcbyRqaW5pYmJiaWFMXGqAHbcNMw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2597944498</pqid></control><display><type>article</type><title>Memotion Analysis through the Lens of Joint Embedding</title><source>Freely Accessible Journals</source><creator>Gunti, Nethra ; Ramamoorthy, Sathyanarayanan ; Patwa, Parth ; Das, Amitava</creator><creatorcontrib>Gunti, Nethra ; Ramamoorthy, Sathyanarayanan ; Patwa, Parth ; Das, Amitava</creatorcontrib><description>Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be used to spread hate and fake information. That along with its growing ubiquity over several social platforms has caused automatic analysis of memes to become a widespread topic of research. In this paper, we report our initial experiments on Memotion Analysis problem through joint embeddings. Results are marginally yielding SOTA.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Embedding ; Modal data</subject><ispartof>arXiv.org, 2021-12</ispartof><rights>2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Gunti, Nethra</creatorcontrib><creatorcontrib>Ramamoorthy, Sathyanarayanan</creatorcontrib><creatorcontrib>Patwa, Parth</creatorcontrib><creatorcontrib>Das, Amitava</creatorcontrib><title>Memotion Analysis through the Lens of Joint Embedding</title><title>arXiv.org</title><description>Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be used to spread hate and fake information. That along with its growing ubiquity over several social platforms has caused automatic analysis of memes to become a widespread topic of research. In this paper, we report our initial experiments on Memotion Analysis problem through joint embeddings. Results are marginally yielding SOTA.</description><subject>Embedding</subject><subject>Modal data</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mQw9U3NzS_JzM9TcMxLzKkszixWKMkoyi9NzwDSqQo-qXnFCvlpCl75mXklCq65SakpKZl56TwMrGmJOcWpvFCam0HZzTXE2UO3oCi_sDS1uCQ-K7-0CGhgcbyRqaW5pYmJiaWFMXGqAHbcNMw</recordid><startdate>20211203</startdate><enddate>20211203</enddate><creator>Gunti, Nethra</creator><creator>Ramamoorthy, Sathyanarayanan</creator><creator>Patwa, Parth</creator><creator>Das, Amitava</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20211203</creationdate><title>Memotion Analysis through the Lens of Joint Embedding</title><author>Gunti, Nethra ; Ramamoorthy, Sathyanarayanan ; Patwa, Parth ; Das, Amitava</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_25979444983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Embedding</topic><topic>Modal data</topic><toplevel>online_resources</toplevel><creatorcontrib>Gunti, Nethra</creatorcontrib><creatorcontrib>Ramamoorthy, Sathyanarayanan</creatorcontrib><creatorcontrib>Patwa, Parth</creatorcontrib><creatorcontrib>Das, Amitava</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gunti, Nethra</au><au>Ramamoorthy, Sathyanarayanan</au><au>Patwa, Parth</au><au>Das, Amitava</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Memotion Analysis through the Lens of Joint Embedding</atitle><jtitle>arXiv.org</jtitle><date>2021-12-03</date><risdate>2021</risdate><eissn>2331-8422</eissn><abstract>Joint embedding (JE) is a way to encode multi-modal data into a vector space where text remains as the grounding key and other modalities like image are to be anchored with such keys. Meme is typically an image with embedded text onto it. Although, memes are commonly used for fun, they could also be used to spread hate and fake information. That along with its growing ubiquity over several social platforms has caused automatic analysis of memes to become a widespread topic of research. In this paper, we report our initial experiments on Memotion Analysis problem through joint embeddings. Results are marginally yielding SOTA.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2021-12
issn 2331-8422
language eng
recordid cdi_proquest_journals_2597944498
source Freely Accessible Journals
subjects Embedding
Modal data
title Memotion Analysis through the Lens of Joint Embedding
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T03%3A28%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Memotion%20Analysis%20through%20the%20Lens%20of%20Joint%20Embedding&rft.jtitle=arXiv.org&rft.au=Gunti,%20Nethra&rft.date=2021-12-03&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2597944498%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2597944498&rft_id=info:pmid/&rfr_iscdi=true