Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach

Guillaume Daval-Frerot, Abdesselam Bouchekif, Anatole Moreau


Abstract
In this paper we present our system for detecting valence task. The major issue was to apply a state-of-the-art system despite the small dataset provided: the system would quickly overfit. The main idea of our proposal is to use transfer learning, which allows to avoid learning from scratch. Indeed, we start to train a first model to predict if a tweet is positive, negative or neutral. For this we use an external dataset which is larger and similar to the target dataset. Then, the pre-trained model is re-used as the starting point to train a new model that classifies a tweet into one of the seven various levels of sentiment intensity. Our system, trained using transfer learning, achieves 0.776 and 0.763 respectively for Pearson correlation coefficient and weighted quadratic kappa metrics on the subtask evaluation dataset.
Anthology ID:
S18-1021
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
151–155
Language:
URL:
https://www.aclweb.org/anthology/S18-1021
DOI:
10.18653/v1/S18-1021
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1021.pdf