@inproceedings{suciati-budi-2020-ui,
    title = "{UI} at {S}em{E}val-2020 Task 8: Text-Image Fusion for Sentiment Classification",
    author = "Suciati, Andi  and
      Budi, Indra",
    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://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.158/",
    doi = "10.18653/v1/2020.semeval-1.158",
    pages = "1195--1200",
    abstract = "This paper describes our system, UI, for task A: Sentiment Classification in SemEval-2020 Task 8 Memotion Analysis. We use a common traditional machine learning, which is SVM, by utilizing the combination of text and images features. The data consist text that extracted from memes and the images of memes. We employ n-gram language model for text features and pre-trained model,VGG-16,for image features. After obtaining both features from text and images in form of 2-dimensional arrays, we concatenate and classify the final features using SVM. The experiment results show SVM achieved 35{\%} for its F1 macro, which is 0.132 points or 13.2{\%} above the baseline model."
}