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        <namePart type="given">Zehao</namePart>
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    <abstract>Memes are widely used on social media. They usually contain multi-modal information such as images and texts, serving as valuable data sources to analyse opinions and sentiment orientations of online communities. The provided memes data often face an imbalanced data problem, that is, some classes or labelled sentiment categories significantly outnumber other classes. This often results in difficulty in applying machine learning techniques where balanced labelled input data are required. In this paper, a Gaussian Mixture Model sampling method is proposed to tackle the problem of class imbalance for the memes sentiment classification task. To utilise both text and image data, a multi-modal CNN-LSTM model is proposed to jointly learn latent features for positive, negative and neutral category predictions. The experiments show that the re-sampling model can slightly improve the accuracy on the trial data of sub-task A of Task 8. The multi-modal CNN-LSTM model can achieve macro F1 score 0.329 on the test set.</abstract>
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%0 Conference Proceedings
%T UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis
%A Liu, Zehao
%A Osei-Brefo, Emmanuel
%A Chen, Siyuan
%A Liang, Huizhi
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 dec
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F liu-etal-2020-uor
%X Memes are widely used on social media. They usually contain multi-modal information such as images and texts, serving as valuable data sources to analyse opinions and sentiment orientations of online communities. The provided memes data often face an imbalanced data problem, that is, some classes or labelled sentiment categories significantly outnumber other classes. This often results in difficulty in applying machine learning techniques where balanced labelled input data are required. In this paper, a Gaussian Mixture Model sampling method is proposed to tackle the problem of class imbalance for the memes sentiment classification task. To utilise both text and image data, a multi-modal CNN-LSTM model is proposed to jointly learn latent features for positive, negative and neutral category predictions. The experiments show that the re-sampling model can slightly improve the accuracy on the trial data of sub-task A of Task 8. The multi-modal CNN-LSTM model can achieve macro F1 score 0.329 on the test set.
%R 10.18653/v1/2020.semeval-1.159
%U https://aclanthology.org/2020.semeval-1.159
%U https://doi.org/10.18653/v1/2020.semeval-1.159
%P 1201-1207

Markdown (Informal)

[UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis](https://aclanthology.org/2020.semeval-1.159) (Liu et al., SemEval 2020)

• UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis (Liu et al., SemEval 2020)

ACL

• Zehao Liu, Emmanuel Osei-Brefo, Siyuan Chen, and Huizhi Liang. 2020. UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1201–1207, Barcelona (online). International Committee for Computational Linguistics.