PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis

Sarracén, Gretel Liz De la Peña; Rosso, Paolo; Giachanou, Anastasia

doi:10.18653/v1/2020.semeval-1.115

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Dataset [12] [11] [12-9] OFF19 0.789 0.770 0.779 OFF20 0.895 0.894 0.885 A. Implication of the parameter ϵ.…”

Section: Layersmentioning

confidence: 99%

On the Keyword Extraction and Bias Analysis, Graph-based Exploration and Data Augmentation for Abusive Language Detection in Low-Resource Settings

Peña Sarracén

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Many thanks go to my colleagues and fellow researchers at the Universitat Politècnica de València, whose collaboration and support have been both enjoyable and intellectually enriching. The synergy between academia and industry fostered by Symanto has been instrumental in achieving the objectives of this thesis.I extend my appreciation to the reviewers of this thesis Dr. Arkaitz Zubiaga (Queen Mary University of London), Prof. Rafael Valencia (Universidad de Murcia), and Dr. Tommaso Caselli (Rijksuniveristeit Groningen); thank you very much for your work on my thesis. I would also like to thank the members of the PhD committee of this thesis Dr. Arkaitz Zubiaga (Queen Mary University of London), Dra. Sara Tonelli (Fondazione Bruno Kessler), and Dra. Aitziber Atutxa (University of the Basque Country).Special thanks go to my family for their tireless encouragement and understanding. Their love and support have been my pillars throughout this challenging yet rewarding endeavor. Also to the friends who have taken an interest in my emotional and physical well-being during my doctoral research.Last but not least, I would like to thank all the participants and individuals who generously contributed their time. I am truly grateful.

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“…Dataset [12] [11] [12-9] OFF19 0.789 0.770 0.779 OFF20 0.895 0.894 0.885 A. Implication of the parameter ϵ.…”

Section: Layersmentioning

confidence: 99%

On the Keyword Extraction and Bias Analysis, Graph-based Exploration and Data Augmentation for Abusive Language Detection in Low-Resource Settings

Peña Sarracén

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Exercise? I thought you said 'Extra Fries’: Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis

Pramanick

Akhtar

Chakraborty

2021

ICWSM

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Today's Internet is awash in memes as they are humorous, satirical, or ironic which make people laugh. According to a survey, 33% of social media users in age bracket [13-35] send memes every day, whereas more than 50% send every week. Some of these memes spread rapidly within a very short time-frame, and their virality depends on the novelty of their (textual and visual) content. A few of them convey positive messages, such as funny or motivational quotes; while others are meant to mock/hurt someone's feelings through sarcastic or offensive messages. Despite the appealing nature of memes and their rapid emergence on social media, effective analysis of memes has not been adequately attempted to the extent it deserves. Recently, in SemEval'20, a pioneering attempt has been made in this direction by organizing a shared task on `Memotion Analysis' (meme emotion analysis). As expected, the competition attracted more than 500 participants with the final submission of [23-32] systems across three sub-tasks. In this paper, we attempt to solve the same set of tasks suggested in the SemEval'20 - Memotion Analysis competition. We propose a multi-hop attention-based deep neural network framework, called MHA-Meme, whose prime objective is to leverage the spatial-domain correspondence between the visual modality (an image) and various textual segments to extract fine-grained feature representations for classification. We evaluate MHA-Meme on the `Memotion Analysis' dataset for all three sub-tasks - sentiment classification, affect classification, and affect class quantification. Our comparative study shows state-of-the-art performances of MHA-Meme for all three tasks compared to the top systems that participated in the competition. Unlike all the baselines which perform inconsistently across all three tasks, MHA-Meme outperforms baselines in all the tasks on average. Moreover, we validate the generalization of MHA-Meme on another set of manually annotated test samples and observe it to be consistent. Finally, we establish the interpretability of MHA-Meme.

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SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!

Sharma¹,

Bhageria²,

Scott³

et al. 2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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Information on social media comprises of various modalities such as textual, visual and audio. NLP and Computer Vision communities often leverage only one prominent modality in isolation to study social media. However, computational processing of Internet memes needs a hybrid approach. The growing ubiquity of Internet memes on social media platforms such as Facebook, Instagram, and Twitter further suggests that we can not ignore such multimodal content anymore. To the best of our knowledge, there is not much attention towards meme emotion analysis. The objective of this proposal is to bring the attention of the research community towards the automatic processing of Internet memes. The task Memotion analysis released approx 10K annotated memes-with human annotated labels namely sentiment(positive, negative, neutral), type of emotion(sarcastic,funny,offensive, motivation) and their corresponding intensity. The challenge consisted of three subtasks: sentiment (positive, negative, and neutral) analysis of memes, overall emotion (humor, sarcasm, offensive, and motivational) classification of memes, and classifying intensity of meme emotion. The best performances achieved were F 1 (macro average) scores of 0.35, 0.51 and 0.32, respectively for each of the three subtasks.

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PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis

Cited by 4 publications

References 12 publications

On the Keyword Extraction and Bias Analysis, Graph-based Exploration and Data Augmentation for Abusive Language Detection in Low-Resource Settings

On the Keyword Extraction and Bias Analysis, Graph-based Exploration and Data Augmentation for Abusive Language Detection in Low-Resource Settings

Exercise? I thought you said 'Extra Fries’: Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis

SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!

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