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            <title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
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            <namePart type="given">Aurelie</namePart>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>Recent technological advancements in the Internet and Social media usage have resulted in the evolution of faster and efficient platforms of communication. These platforms include visual, textual and speech mediums and have brought a unique social phenomenon called Internet memes. Internet memes are in the form of images with witty, catchy, or sarcastic text descriptions. In this paper, we present a multi-modal sentiment analysis system using deep neural networks combining Computer Vision and Natural Language Processing. Our aim is different than the normal sentiment analysis goal of predicting whether a text expresses positive or negative sentiment; instead, we aim to classify the Internet meme as a positive, negative, or neutral, identify the type of humor expressed and quantify the extent to which a particular effect is being expressed. Our system has been developed using CNN and LSTM and outperformed the baseline score.</abstract>
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%0 Conference Proceedings
%T Gundapusunil at SemEval-2020 Task 8: Multimodal Memotion Analysis
%A Gundapu, Sunil
%A Mamidi, Radhika
%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 gundapu-mamidi-2020-gundapusunil
%X Recent technological advancements in the Internet and Social media usage have resulted in the evolution of faster and efficient platforms of communication. These platforms include visual, textual and speech mediums and have brought a unique social phenomenon called Internet memes. Internet memes are in the form of images with witty, catchy, or sarcastic text descriptions. In this paper, we present a multi-modal sentiment analysis system using deep neural networks combining Computer Vision and Natural Language Processing. Our aim is different than the normal sentiment analysis goal of predicting whether a text expresses positive or negative sentiment; instead, we aim to classify the Internet meme as a positive, negative, or neutral, identify the type of humor expressed and quantify the extent to which a particular effect is being expressed. Our system has been developed using CNN and LSTM and outperformed the baseline score.
%R 10.18653/v1/2020.semeval-1.147
%U https://aclanthology.org/2020.semeval-1.147/
%U https://doi.org/10.18653/v1/2020.semeval-1.147
%P 1112-1119

Markdown (Informal)

[Gundapusunil at SemEval-2020 Task 8: Multimodal Memotion Analysis](https://aclanthology.org/2020.semeval-1.147/) (Gundapu & Mamidi, SemEval 2020)

• Gundapusunil at SemEval-2020 Task 8: Multimodal Memotion Analysis (Gundapu & Mamidi, SemEval 2020)

ACL

• Sunil Gundapu and Radhika Mamidi. 2020. Gundapusunil at SemEval-2020 Task 8: Multimodal Memotion Analysis. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1112–1119, Barcelona (online). International Committee for Computational Linguistics.