Challenges for Open-domain Targeted Sentiment Analysis

• URL: http://arxiv.org/abs/2204.06893v2
• Date: Fri, 15 Apr 2022 06:08:44 GMT
• Title: Challenges for Open-domain Targeted Sentiment Analysis
• Authors: Yun Luo and Hongjie Cai and Linyi Yang and Yanxia Qin and Rui Xia and Yue Zhang
• Abstract summary: We propose a novel dataset consisting of 6,013 human-labeled data to extend the data domains in topics of interest and document level. We also offer a nested target annotation schema to extract the complete sentiment information in documents.
• Score: 21.61943346030794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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