UI at SemEval-2020 Task 8: Text-Image Fusion for Sentiment Classification

Andi Suciati, Indra Budi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings
EditorsAurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
PublisherInternational Committee for Computational Linguistics
Pages1195-1200
Number of pages6
ISBN (Electronic)9781952148316
Publication statusPublished - 2020
Event14th International Workshops on Semantic Evaluation, SemEval 2020 - Barcelona, Spain
Duration: 12 Dec 202013 Dec 2020

Publication series

Name14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings

Conference

Conference14th International Workshops on Semantic Evaluation, SemEval 2020
Country/TerritorySpain
CityBarcelona
Period12/12/2013/12/20

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