@inproceedings{gupta-etal-2020-bennettnlp,
title = "{B}ennett{NLP} at {S}em{E}val-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier",
author = "Gupta, Ambuje and
Kataria, Harsh and
Mishra, Souvik and
Badal, Tapas and
Mishra, Vipul",
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-1.143",
doi = "10.18653/v1/2020.semeval-1.143",
pages = "1085--1093",
abstract = {Memotion analysis is a very crucial and important subject in today{'}s world that is dominated by social media. This paper presents the results and analysis of the SemEval-2020 Task-8: Memotion analysis by team Kraken that qualified as winners for the task. This involved performing multimodal sentiment analysis on memes commonly posted over social media. The task comprised of 3 subtasks, Task A was to find the overall sentiment of a meme and classify it into positive, negative or neutral, Task B was to classify it into the different types which were namely humour, sarcasm, offensive or motivation where a meme could have more than one category, Task C was to further quantify the classifications achieved in task B. An imbalanced data of 6992 rows was utilized for this which contained images (memes), text (extracted OCR) and their annotations in 17 classes provided by the task organisers. In this paper, the authors proposed a hybrid neural Na{\"\i}ve-Bayes Support Vector Machine and logistic regression to solve a multilevel 17 class classification problem. It achieved the best result in Task B i.e 0.70 F1 score. The authors were ranked third in Task B.},
}
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<abstract>Memotion analysis is a very crucial and important subject in today’s world that is dominated by social media. This paper presents the results and analysis of the SemEval-2020 Task-8: Memotion analysis by team Kraken that qualified as winners for the task. This involved performing multimodal sentiment analysis on memes commonly posted over social media. The task comprised of 3 subtasks, Task A was to find the overall sentiment of a meme and classify it into positive, negative or neutral, Task B was to classify it into the different types which were namely humour, sarcasm, offensive or motivation where a meme could have more than one category, Task C was to further quantify the classifications achieved in task B. An imbalanced data of 6992 rows was utilized for this which contained images (memes), text (extracted OCR) and their annotations in 17 classes provided by the task organisers. In this paper, the authors proposed a hybrid neural Naïve-Bayes Support Vector Machine and logistic regression to solve a multilevel 17 class classification problem. It achieved the best result in Task B i.e 0.70 F1 score. The authors were ranked third in Task B.</abstract>
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%0 Conference Proceedings
%T BennettNLP at SemEval-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier
%A Gupta, Ambuje
%A Kataria, Harsh
%A Mishra, Souvik
%A Badal, Tapas
%A Mishra, Vipul
%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 gupta-etal-2020-bennettnlp
%X Memotion analysis is a very crucial and important subject in today’s world that is dominated by social media. This paper presents the results and analysis of the SemEval-2020 Task-8: Memotion analysis by team Kraken that qualified as winners for the task. This involved performing multimodal sentiment analysis on memes commonly posted over social media. The task comprised of 3 subtasks, Task A was to find the overall sentiment of a meme and classify it into positive, negative or neutral, Task B was to classify it into the different types which were namely humour, sarcasm, offensive or motivation where a meme could have more than one category, Task C was to further quantify the classifications achieved in task B. An imbalanced data of 6992 rows was utilized for this which contained images (memes), text (extracted OCR) and their annotations in 17 classes provided by the task organisers. In this paper, the authors proposed a hybrid neural Naïve-Bayes Support Vector Machine and logistic regression to solve a multilevel 17 class classification problem. It achieved the best result in Task B i.e 0.70 F1 score. The authors were ranked third in Task B.
%R 10.18653/v1/2020.semeval-1.143
%U https://aclanthology.org/2020.semeval-1.143
%U https://doi.org/10.18653/v1/2020.semeval-1.143
%P 1085-1093
Markdown (Informal)
[BennettNLP at SemEval-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier](https://aclanthology.org/2020.semeval-1.143) (Gupta et al., SemEval 2020)
ACL