Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese

Rogério Sousa, Thiago Pardo


Abstract
Over the years, the review helpfulness prediction task has been the subject of several works, but remains being a challenging issue in Natural Language Processing, as results vary a lot depending on the domain, on the adopted features and on the chosen classification strategy. This paper attempts to evaluate the impact of content features and classification methods for two different domains. In particular, we run our experiments for a low resource language – Portuguese –, trying to establish a benchmark for this language. We show that simple features and classical classification methods are powerful for the task of helpfulness prediction, but are largely outperformed by a convolutional neural network-based solution.
Anthology ID:
2022.wassa-1.19
Volume:
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Jeremy Barnes, Orphée De Clercq, Valentin Barriere, Shabnam Tafreshi, Sawsan Alqahtani, João Sedoc, Roman Klinger, Alexandra Balahur
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
204–213
Language:
URL:
https://aclanthology.org/2022.wassa-1.19
DOI:
10.18653/v1/2022.wassa-1.19
Bibkey:
Cite (ACL):
Rogério Sousa and Thiago Pardo. 2022. Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 204–213, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese (Sousa & Pardo, WASSA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.wassa-1.19.pdf
Video:
 https://aclanthology.org/2022.wassa-1.19.mp4