This study evaluates the performance of different sentiment analysis methods in the context of public deliberation, focusing on hard-, soft-, and subjective-label scenarios to answer the research question: ``can a Large Language Model detect subjective sentiment of statements within the context of public deliberation?''. If the answer to this question is affirmative, that is a strong indicator that, with the help of longitudinal studies, sentiment analysis with large language models (LLMs) may be implemented to scale public deliberations by providing support for moderators in such discussions. To answer this question, four prompting methods were tested: zero-shot, few-shot, chain-of-thought (CoT) zero-shot, and CoT few-shot using a Frisian dataset of 50 statements annotated by 5 annotators. The findings indicate that the CoT few-shot method significantly outperforms other methods in all scenarios, that soft-labels outperform their hard equivalent, that the underlying data must be balanced for high performing models, and that capturing the perspective of a specific annotator requires further research. Our study suggests that LLMs may perform best under the supervision, or with the collaboration of a human, due to the multi-faced nature of sentiment.

In order to tackle topics such as climate change together with the population, public discourse should be scaled up. This discourse should be mediated as it makes it more likely that people understand each other and change their point of view. To help the mediator with this task, emotion detection can greatly help. Positive emotions can improve communications, while negative emotions cause people to be irrational and irritated. However, since emotions are highly subjective, it can make both predictions and evaluation more difficult.

Still, Large Language Models (LLMs) could be used to detect these subjective emotions using different prompting strategies and labels. The experiment included zero-, one-, fewshot and Chain of Thought (CoT) strategies. The precision was better for the one- and fewshot method compared to zeroshot. The CoT methods also showed an increase in precision, but a decrease in recall. The different labels were hard majority labels, soft labels and hard per annotator labels. In conclusion, providing examples improved the performance of the LLM. The CoT strategies were more precise, but gave a worse general prediction. The hard majority labels allow for more general predictions, where per annotator hard labels capture the perspective of different annotators. Soft labels reflect the subjective nature of the labels by providing probabilities instead of binary classification.

The experiment was done on a small data sample, so it is recommended to try the strategies on a larger data sample. Looking into appropriate evaluations for subjective predictions is also recommended in order to reflect the actual performance better.

This paper investigates the use of Large Language Models (LLMs) for automatic detection of subjective values in argument statements in public discourse. Understanding the underlying values of argument statements could enhance public discussions and potentially lead to better outcomes. The LLM utilization methods tested were zero- and few-shot prompting, as well as chain-of-thought prompts. In order to compare the predictions made by the LLM, a set of ground truth labels was required as an established baseline. For these labels, either single majority labels or multi-value labels were considered, both derived from a set of aggregated human annotations. Results indicated that LLM performance was sub optimal, achieving a maximum weighed F1 score of 0.594 for single-value chain-of-thought predictions. Additionally, current metrics were found inadequate for assessing LLM performance on a highly subjective task such as value detection, evidenced by poor scores in multi-value predictions despite subjective evaluation suggesting otherwise. Furthermore, a last experiment was aimed at capturing a specific annotator’s subjectivity. This yielded inconsistent results, with F1 scores peaking around 0.4, indicating that LLMs are not well-suited for emulating individual human subjectivity.

Public deliberations play a crucial role in democratic systems. However, the unstructured nature of deliberations leads to challenges for moderators to analyze the large volume of data produced. This paper aims to solve this challenge by automatically identifying subjective topics behind public discourse by leveraging Large Language Models (LLMs). The study is structured around two core objectives: Identifying Gold Labels and Exploring Subjective Human Labels. The results highlight that fine-tuning the LLaMa-2 model with QLoRa outperforms other methods for Identifying Gold Labels, while the Few-Shot Chain of Thoughts method, enhanced with EmotionPrompt, is particularly effective in capturing subjective variations in human annotations. However, the study also underscores significant limitations, such as the dependency on large, high-quality annotated datasets and the tendency of models to produce hallucinations. These findings highlight the potential of LLMs to identify subjective topics behind public discourse, while also emphasizing the need for further research to address these challenges.

This study investigates the effectiveness of Large Language Models (LLMs) in identifying and classifying subjective arguments within deliberative discourse. Using data from a Participatory Value Evaluation (PVE) conducted in the Netherlands, this research introduces an annotation strategy for identifying arguments and extracting their premises. Then, the Llama 2 model is used to test three different prompting approaches: zero-shot, one-shot and few-shot. The performance is evaluated using the cosine similarity metric and later enhanced by introducing chain-of-thought prompting. The results show that zero-shot prompting unexpectedly outperforms one-shot and few-shot prompting, due to the LLM overfitting to the examples provided. Chain-of-thought prompting is shown to improve the argument identification task. The subjectivity of the annotation task is reflected by the low averaged pairwise F1 score between annotators, and the considerable variance in the number of data items marked by each annotator as not being arguments. The subjectivity of the task is further highlighted by a pairwise chain-of-thought prompting analysis, which shows that annotators with more similar annotations received more similar LLM responses.