Abstract
Memes act as a medium to carry one’s feelings, cultural ideas, or practices by means of symbols, imitations, or simply images. Whenever social media is involved, hurting the feelings of others and abusing others are always a problem. Here we are proposing a system, that classifies the memes into abusive/offensive memes and neutral ones. The work involved classifying the images into offensive and non-offensive ones. The system implements resnet-50, a deep residual neural network architecture.- Anthology ID:
- 2021.dravidianlangtech-1.39
- Volume:
- Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv
- Editors:
- Bharathi Raja Chakravarthi, Ruba Priyadharshini, Anand Kumar M, Parameswari Krishnamurthy, Elizabeth Sherly
- Venue:
- DravidianLangTech
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 277–280
- Language:
- URL:
- https://aclanthology.org/2021.dravidianlangtech-1.39
- DOI:
- Cite (ACL):
- Manoj Balaji J and Chinmaya Hs. 2021. TrollMeta@DravidianLangTech-EACL2021: Meme classification using deep learning. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 277–280, Kyiv. Association for Computational Linguistics.
- Cite (Informal):
- TrollMeta@DravidianLangTech-EACL2021: Meme classification using deep learning (J & Hs, DravidianLangTech 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.dravidianlangtech-1.39.pdf
- Data
- Tamil Memes