MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes

Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque


Abstract
Posting and sharing memes have become a powerful expedient of expressing opinions on social media in recent days. Analysis of sentiment from memes has gained much attention to researchers due to its substantial implications in various domains like finance and politics. Past studies on sentiment analysis of memes have primarily been conducted in English, where low-resource languages gain little or no attention. However, due to the proliferation of social media usage in recent years, sentiment analysis of memes is also a crucial research issue in low resource languages. The scarcity of benchmark datasets is a significant barrier to performing multimodal sentiment analysis research in resource-constrained languages like Bengali. This paper presents a novel multimodal dataset (named MemoSen) for Bengali containing 4417 memes with three annotated labels positive, negative, and neutral. A detailed annotation guideline is provided to facilitate further resource development in this domain. Additionally, a set of experiments are carried out on MemoSen by constructing twelve unimodal (i.e., visual, textual) and ten multimodal (image+text) models. The evaluation exhibits that the integration of multimodal information significantly improves (about 1.2%) the meme sentiment classification compared to the unimodal counterparts and thus elucidate the novel aspects of multimodality.
Anthology ID:
2022.lrec-1.165
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1542–1554
Language:
URL:
https://aclanthology.org/2022.lrec-1.165
DOI:
Bibkey:
Cite (ACL):
Eftekhar Hossain, Omar Sharif, and Mohammed Moshiul Hoque. 2022. MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1542–1554, Marseille, France. European Language Resources Association.
Cite (Informal):
MemoSen: A Multimodal Dataset for Sentiment Analysis of Memes (Hossain et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/auto-file-uploads/2022.lrec-1.165.pdf
Data
Visual Question Answering