Affect inTweets: A Transfer Learning Approach

Linrui Zhang, Hsin-Lun Huang, Yang Yu, Dan Moldovan


Abstract
People convey sentiments and emotions through language. To understand these affectual states is an essential step towards understanding natural language. In this paper, we propose a transfer-learning based approach to inferring the affectual state of a person from their tweets. As opposed to the traditional machine learning models which require considerable effort in designing task specific features, our model can be well adapted to the proposed tasks with a very limited amount of fine-tuning, which significantly reduces the manual effort in feature engineering. We aim to show that by leveraging the pre-learned knowledge, transfer learning models can achieve competitive results in the affectual content analysis of tweets, compared to the traditional models. As shown by the experiments on SemEval-2018 Task 1: Affect in Tweets, our model ranking 2nd, 4th and 6th place in four of its subtasks proves the effectiveness of our idea.
Anthology ID:
2020.lrec-1.188
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1511–1516
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.188
DOI:
Bibkey:
Cite (ACL):
Linrui Zhang, Hsin-Lun Huang, Yang Yu, and Dan Moldovan. 2020. Affect inTweets: A Transfer Learning Approach. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1511–1516, Marseille, France. European Language Resources Association.
Cite (Informal):
Affect inTweets: A Transfer Learning Approach (Zhang et al., LREC 2020)
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PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2020.lrec-1.188.pdf