@inproceedings{walinska-potoniec-2020-urszula,
title = "Urszula Wali{\'n}ska at {S}em{E}val-2020 Task 8: Fusion of Text and Image Features Using {LSTM} and {VGG}16 for Memotion Analysis",
author = "Wali{\'n}ska, Urszula and
Potoniec, J{\k{e}}drzej",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.161",
doi = "10.18653/v1/2020.semeval-1.161",
pages = "1215--1220",
abstract = "In this paper, we describe the entry to the task of Memotion Analysis. The sentiment analysis of memes task, is motivated by a pervasive problem of offensive content spread in social media, up to the present time. In fact, memes are an important medium of expressing opinion and emotions, therefore they can be hateful at many times. In order to identify emotions expressed by memes we construct a tool based on neural networks and deep learning methods. It takes an advantage of a multi-modal nature of the task and performs fusion of image and text features extracted by models dedicated to this task. Moreover, we show that visual information might be more significant in the sentiment analysis of memes than textual one. Our solution achieved 0.346 macro F1-score in Task A {--} Sentiment Classification, which brought us to the 7th place in the official rank of the competition.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="walinska-potoniec-2020-urszula">
<titleInfo>
<title>Urszula Walińska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis</title>
</titleInfo>
<name type="personal">
<namePart type="given">Urszula</namePart>
<namePart type="family">Walińska</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jędrzej</namePart>
<namePart type="family">Potoniec</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aurelie</namePart>
<namePart type="family">Herbelot</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaodan</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Schneider</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jonathan</namePart>
<namePart type="family">May</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>International Committee for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Barcelona (online)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper, we describe the entry to the task of Memotion Analysis. The sentiment analysis of memes task, is motivated by a pervasive problem of offensive content spread in social media, up to the present time. In fact, memes are an important medium of expressing opinion and emotions, therefore they can be hateful at many times. In order to identify emotions expressed by memes we construct a tool based on neural networks and deep learning methods. It takes an advantage of a multi-modal nature of the task and performs fusion of image and text features extracted by models dedicated to this task. Moreover, we show that visual information might be more significant in the sentiment analysis of memes than textual one. Our solution achieved 0.346 macro F1-score in Task A – Sentiment Classification, which brought us to the 7th place in the official rank of the competition.</abstract>
<identifier type="citekey">walinska-potoniec-2020-urszula</identifier>
<identifier type="doi">10.18653/v1/2020.semeval-1.161</identifier>
<location>
<url>https://aclanthology.org/2020.semeval-1.161</url>
</location>
<part>
<date>2020-12</date>
<extent unit="page">
<start>1215</start>
<end>1220</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Urszula Walińska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis
%A Walińska, Urszula
%A Potoniec, Jędrzej
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F walinska-potoniec-2020-urszula
%X In this paper, we describe the entry to the task of Memotion Analysis. The sentiment analysis of memes task, is motivated by a pervasive problem of offensive content spread in social media, up to the present time. In fact, memes are an important medium of expressing opinion and emotions, therefore they can be hateful at many times. In order to identify emotions expressed by memes we construct a tool based on neural networks and deep learning methods. It takes an advantage of a multi-modal nature of the task and performs fusion of image and text features extracted by models dedicated to this task. Moreover, we show that visual information might be more significant in the sentiment analysis of memes than textual one. Our solution achieved 0.346 macro F1-score in Task A – Sentiment Classification, which brought us to the 7th place in the official rank of the competition.
%R 10.18653/v1/2020.semeval-1.161
%U https://aclanthology.org/2020.semeval-1.161
%U https://doi.org/10.18653/v1/2020.semeval-1.161
%P 1215-1220
Markdown (Informal)
[Urszula Walińska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis](https://aclanthology.org/2020.semeval-1.161) (Walińska & Potoniec, SemEval 2020)
ACL