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        <title>Only text? only image? or both? Predicting sentiment of internet memes</title>
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        <namePart type="given">Pranati</namePart>
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        <namePart type="given">Mamta</namePart>
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    <name type="personal">
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            <title>Proceedings of the 17th International Conference on Natural Language Processing (ICON)</title>
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    <abstract>Nowadays, the spread of Internet memes on online social media platforms such as Instagram, Facebook, Reddit, and Twitter is very fast. Analyzing the sentiment of memes can provide various useful insights. Meme sentiment classification is a new area of research that is not explored yet. Recently SemEval provides a dataset for meme sentiment classification. As this dataset is highly imbalanced, we extend this dataset by annotating new instances and use a sampling strategy to build a meme sentiment classifier. We propose a multi-modal framework for meme sentiment classification by utilizing textual and visual features of the meme. We found that for meme sentiment classification, only textual or only visual features are not sufficient. Our proposed framework utilizes textual as well as visual features together. We propose to use the attention mechanism to improve meme classification performance. Our proposed framework achieves macro F1 and accuracy of 34.23 and 50.02, respectively. It increases the accuracy by 6.77 and 7.86 compared to only textual and visual features, respectively.</abstract>
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        <url>https://aclanthology.org/2020.icon-main.60</url>
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        <date>2020-dec</date>
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            <start>444</start>
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%0 Conference Proceedings
%T Only text? only image? or both? Predicting sentiment of internet memes
%A Behera, Pranati
%A ., Mamta
%A Ekbal, Asif
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 dec
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F behera-etal-2020-text
%X Nowadays, the spread of Internet memes on online social media platforms such as Instagram, Facebook, Reddit, and Twitter is very fast. Analyzing the sentiment of memes can provide various useful insights. Meme sentiment classification is a new area of research that is not explored yet. Recently SemEval provides a dataset for meme sentiment classification. As this dataset is highly imbalanced, we extend this dataset by annotating new instances and use a sampling strategy to build a meme sentiment classifier. We propose a multi-modal framework for meme sentiment classification by utilizing textual and visual features of the meme. We found that for meme sentiment classification, only textual or only visual features are not sufficient. Our proposed framework utilizes textual as well as visual features together. We propose to use the attention mechanism to improve meme classification performance. Our proposed framework achieves macro F1 and accuracy of 34.23 and 50.02, respectively. It increases the accuracy by 6.77 and 7.86 compared to only textual and visual features, respectively.
%U https://aclanthology.org/2020.icon-main.60
%P 444-452

Markdown (Informal)

[Only text? only image? or both? Predicting sentiment of internet memes](https://aclanthology.org/2020.icon-main.60) (Behera et al., ICON 2020)

• Only text? only image? or both? Predicting sentiment of internet memes (Behera et al., ICON 2020)

ACL

• Pranati Behera, Mamta ., and Asif Ekbal. 2020. Only text? only image? or both? Predicting sentiment of internet memes. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 444–452, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).