Parsing Complex Political Opinions with Large Language Models: Cutting-Edge Exploration of Target-Stance Extraction Technology

This article focuses on the application of Large Language Models (LLMs) in political text analysis, with the core being the exploration of Target-Stance Extraction (TSE) technology. Addressing the coarse-grained limitations of traditional sentiment analysis, the study leverages the semantic understanding and reasoning capabilities of LLMs to solve problems such as multi-target identification, stance determination, and complex context processing in political texts, providing a new path for political analysis and public opinion monitoring. The following sections will elaborate on background, methodology, experiments, applications, and other aspects.

In the field of political text analysis, traditional sentiment analysis can only provide an overall emotional tendency and cannot accurately identify specific stances towards particular issues or entities, leading to significant information loss when dealing with complex political discussions. The Target-Stance Extraction (TSE) task emerged to address this, requiring models to identify political entities/issues and determine the author's stance. However, this task faces challenges: political language is implicit and rich in metaphors, and stance expression depends heavily on context.

In recent years, LLMs have demonstrated strong text understanding and reasoning capabilities. Compared to traditional rule-based or shallow machine learning methods, they are better at capturing deep semantics and context dependencies, making them suitable for handling the complexity of political texts. The research team explored the application potential of LLMs to address the core challenges of the TSE task: multi-target identification, stance determination, and complex context processing.

The study constructed a complete TSE workflow:

1. Prompt Engineering Strategy: Design structured prompt templates to improve output parseability and task understanding;
2. Chain-of-Thought Reasoning Enhancement: Use intermediate reasoning steps to significantly improve stance recognition accuracy;
3. Domain Adaptation Optimization: Select domain-specific examples and optimize prompts to meet the needs of political analysis scenarios.

The study was evaluated on multiple political text datasets, and the results showed:

• LLMs significantly outperformed traditional supervised learning methods on the TSE task;
• Chain-of-thought reasoning technology brought a noticeable performance improvement;
• The model had good robustness in texts with multiple targets and stances;
• It had a certain ability to understand complex expressions such as metaphors and sarcasm.

The technology has broad application prospects:

• Public Opinion Monitoring and Analysis: Real-time monitoring of the distribution of public attitudes towards policies/events;
• Election Research: Analyzing voters' complex attitudes towards candidates and issues;
• Media Bias Detection: Identifying target-stance patterns in news reports;
• Policy Effect Evaluation: Tracking changes in public reactions to new policies.

The current method has limitations: data bias, insufficient quantification of stance intensity, unvalidated cross-language applicability, and the need to improve causal reasoning capabilities. Future directions include: expanding to multilingual scenarios, introducing stance intensity modeling, enhancing reasoning with knowledge graphs, and exploring efficient fine-tuning strategies.

This study demonstrates the great potential of LLMs in the field of political text analysis. Through TSE technology, structured opinion data can be extracted from massive political texts, providing a new tool for understanding complex political phenomena. As the technology matures, more innovative applications are expected in political science, public opinion analysis, public policy, and other fields.