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.
Anthology ID:
2021.findings-emnlp.53
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
584–590
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.53
DOI:
10.18653/v1/2021.findings-emnlp.53
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.53.pdf
Code
 webis-de/emnlp-21