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The advancement of generative AI, involving the utilization of large language models (LLMs) like ChatGPT to assess public opinion and sentiment, has become increasingly prevalent. However, this upsurge in usage raises significant questions about the transparency and interpretability of the predictions made by these LLM Models. Hence, this paper explores the imperative of ensuring transparency in the application of ChatGPT for public sentiment analysis. To tackle these challenges, we propose using a lexicon-based model as a surrogate to approximate both global and local predictions. Through case studies, we demonstrate how transparency mechanisms, bolstered by the lexicon-based model, can be seamlessly integrated into ChatGPT’s deployment for sentiment analysis. Drawing on the results of our study, we further discuss the implications for future research involving the utilization of LLMs in governmental functions, policymaking, and public engagement.
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Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon
Sentiment lexicons are instrumental for sentiment analysis. One can use a set of sentiment words provided in a sentiment lexicon and a lexicon-based classifier to perform sentiment analysis. One major issue with this approach is that many sentiment words (from the lexicon) are domain dependent. That is, they may be positive in some domains but negative in some others. We refer to this problem as domain polarity-changes of words from a sentiment lexicon. Detecting such words and correcting their sentiment for an application domain is very important. In this paper, we propose a graph-based technique to tackle this problem. Experimental results show its effectiveness on multiple datasets from different domains.
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- Award ID(s):
- 1910424
- PAR ID:
- 10302863
- Date Published:
- Journal Name:
- Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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