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.- Anthology ID:
- 2020.semeval-1.158
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1195–1200
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.158
- DOI:
- 10.18653/v1/2020.semeval-1.158
- Cite (ACL):
- Andi Suciati and Indra Budi. 2020. UI at SemEval-2020 Task 8: Text-Image Fusion for Sentiment Classification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1195–1200, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- UI at SemEval-2020 Task 8: Text-Image Fusion for Sentiment Classification (Suciati & Budi, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.semeval-1.158.pdf