Since previous studies on open-domain targeted sentiment analysis are limited in dataset domain variety and sentence level, we propose a novel dataset consisting of 6,013 human-labeled data to extend the data domains in topics of interest and document level. Furthermore, we offer a nested target annotation schema to extract the complete sentiment information in documents, boosting the practicality and effectiveness of open-domain targeted sentiment analysis. Moreover, we leverage the pre-trained model BART in a sequence-to-sequence generation method for the task. Benchmark results show that there exists large room for improvement of open-domain targeted sentiment analysis. Meanwhile, experiments have shown that challenges remain in the effective use of open-domain data, long documents, the complexity of target structure, and domain variances.
@article{arxiv.2204.06893,
title = {Challenges for Open-domain Targeted Sentiment Analysis},
author = {Yun Luo and Hongjie Cai and Linyi Yang and Yanxia Qin and Rui Xia and Yue Zhang},
journal= {arXiv preprint arXiv:2204.06893},
year = {2022}
}