@inproceedings{boinepelli-etal-2020-sis,
title = "{SIS}@{IIITH} at {S}em{E}val-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis",
author = "Boinepelli, Sravani and
Shrivastava, Manish and
Varma, Vasudeva",
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-1.157",
doi = "10.18653/v1/2020.semeval-1.157",
pages = "1190--1194",
abstract = "Memes are steadily taking over the feeds of the public on social media. There is always the threat of malicious users on the internet posting offensive content, even through memes. Hence, the automatic detection of offensive images/memes is imperative along with detection of offensive text. However, this is a much more complex task as it involves both visual cues as well as language understanding and cultural/context knowledge. This paper describes our approach to the task of SemEval-2020 Task 8: Memotion Analysis. We chose to participate only in Task A which dealt with Sentiment Classification, which we formulated as a text classification problem. Through our experiments, we explored multiple training models to evaluate the performance of simple text classification algorithms on the raw text obtained after running OCR on meme images. Our submitted model achieved an accuracy of 72.69{\%} and exceeded the existing baseline{'}s Macro F1 score by 8{\%} on the official test dataset. Apart from describing our official submission, we shall elucidate how different classification models respond to this task.",
}
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<abstract>Memes are steadily taking over the feeds of the public on social media. There is always the threat of malicious users on the internet posting offensive content, even through memes. Hence, the automatic detection of offensive images/memes is imperative along with detection of offensive text. However, this is a much more complex task as it involves both visual cues as well as language understanding and cultural/context knowledge. This paper describes our approach to the task of SemEval-2020 Task 8: Memotion Analysis. We chose to participate only in Task A which dealt with Sentiment Classification, which we formulated as a text classification problem. Through our experiments, we explored multiple training models to evaluate the performance of simple text classification algorithms on the raw text obtained after running OCR on meme images. Our submitted model achieved an accuracy of 72.69% and exceeded the existing baseline’s Macro F1 score by 8% on the official test dataset. Apart from describing our official submission, we shall elucidate how different classification models respond to this task.</abstract>
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%0 Conference Proceedings
%T SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis
%A Boinepelli, Sravani
%A Shrivastava, Manish
%A Varma, Vasudeva
%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 boinepelli-etal-2020-sis
%X Memes are steadily taking over the feeds of the public on social media. There is always the threat of malicious users on the internet posting offensive content, even through memes. Hence, the automatic detection of offensive images/memes is imperative along with detection of offensive text. However, this is a much more complex task as it involves both visual cues as well as language understanding and cultural/context knowledge. This paper describes our approach to the task of SemEval-2020 Task 8: Memotion Analysis. We chose to participate only in Task A which dealt with Sentiment Classification, which we formulated as a text classification problem. Through our experiments, we explored multiple training models to evaluate the performance of simple text classification algorithms on the raw text obtained after running OCR on meme images. Our submitted model achieved an accuracy of 72.69% and exceeded the existing baseline’s Macro F1 score by 8% on the official test dataset. Apart from describing our official submission, we shall elucidate how different classification models respond to this task.
%R 10.18653/v1/2020.semeval-1.157
%U https://aclanthology.org/2020.semeval-1.157
%U https://doi.org/10.18653/v1/2020.semeval-1.157
%P 1190-1194
Markdown (Informal)
[SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis](https://aclanthology.org/2020.semeval-1.157) (Boinepelli et al., SemEval 2020)
ACL