Urszula Walińska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis

Urszula Walińska, Jędrzej Potoniec


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.
Anthology ID:
2020.semeval-1.161
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1215–1220
Language:
URL:
https://aclanthology.org/2020.semeval-1.161
DOI:
10.18653/v1/2020.semeval-1.161
Bibkey:
Cite (ACL):
Urszula Walińska and Jędrzej Potoniec. 2020. Urszula Walińska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1215–1220, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
Urszula Walińska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis (Walińska & Potoniec, SemEval 2020)
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PDF:
https://aclanthology.org/2020.semeval-1.161.pdf
Data
ImageNet