EMNLP 2021finding5 citations
Casting the Same Sentiment Classification Problem
Erik Körner, Ahmad Dawar Hakimi, Gerhard Heyer, Martin Potthast
Abstract
We introduce and study a problem variant of sentiment analysis, namely the “same sentiment classification problem”, where, given a pair of texts, the task is to determine if they have the same sentiment, disregarding the actual sentiment polarity. Among other things, our goal is to enable a more topic-agnostic sentiment classification. We study the problem using the Yelp business review dataset, demonstrating how sentiment data needs to be prepared for this task, and then carry out sequence pair classification using the BERT language model. In a series of experiments, we achieve an accuracy above 83% for category subsets across topics, and 89% on average.
BibTeX
@inproceedings{korner-etal-2021-casting-sentiment,
title = "Casting the Same Sentiment Classification Problem",
author = {K{\"o}rner, Erik and
Hakimi, Ahmad Dawar and
Heyer, Gerhard and
Potthast, Martin},
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.53/",
doi = "10.18653/v1/2021.findings-emnlp.53",
pages = "584--590"
}