A Hybrid Intelligence Method for Argument Mining: Combining Human and AI for Efficient Opinion Analysis

Core Concepts

HyEnA combines human insight with AI processing to efficiently extract key arguments from opinionated texts.

Abstract

HyEnA, a hybrid method for extracting arguments from opinionated texts, combines human and AI capabilities. It breaks down the extraction task into annotation, consolidation, and selection phases. The method is evaluated on citizen feedback corpora related to COVID-19 measures. Annotators identify unique key arguments while maintaining coherence and diversity in the extracted arguments. The Power algorithm reduces the need for human annotation in identifying similar argument pairs. Louvain and spectral clustering methods are used to create argument clusters based on similarity labels provided by annotators. Clusters show a coherent distribution of arguments with similar stances towards policy options.

Stats

HyEnA achieves higher coverage and precision than automated methods when extracting arguments from diverse opinions.
Annotators identified an average of 15 unique key arguments per option in Phase 1.
The Power algorithm reduced the number of pairs requiring human annotation by 60% in Phase 2.
Louvain clustering yielded the smallest error for two corpora, while spectral clustering performed better for one corpus.
Clusters generally represent a coherent distribution of arguments with similar stances towards policy options.

Quotes

Key Insights Distilled From

A Hybrid Intelligence Method for Argument Mining

by Michiel van ... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.09713.pdf

Deeper Inquiries

How can HyEnA be adapted to analyze opinions on topics beyond COVID-19 measures?

HyEnA can be adapted to analyze opinions on various topics by adjusting the topic modeling and argument extraction components. For different domains, the topic models used in Phase 1 can be retrained or fine-tuned with domain-specific data to generate relevant topics for annotation. Additionally, the key argument annotation phase can be tailored to focus on specific aspects of each topic by providing annotators with guidelines or prompts that are customized for the new domain. The clustering algorithms in Phase 2 can also be optimized based on the characteristics of the new dataset, ensuring that similar arguments are grouped together effectively.

What potential biases or limitations could arise from relying on crowd workers as annotators in HyEnA?

Relying on crowd workers as annotators in HyEnA may introduce biases such as demographic bias, where certain groups of people are overrepresented compared to others. This could lead to a lack of diversity in perspectives captured during annotation. Moreover, there might be inconsistencies in annotations due to varying levels of expertise among crowd workers. Quality control measures must be implemented to ensure consistency and accuracy across annotations. Additionally, there is a risk of malicious behavior or low-quality work from some crowd workers if not properly monitored and managed.

How might incorporating sentiment analysis enhance the insights derived from HyEnA's extracted key arguments?

Incorporating sentiment analysis into HyEnA can provide valuable context and depth to the extracted key arguments by understanding the emotional tone behind each argument. By analyzing sentiments expressed in opinions, HyEnA can identify not just what is being said but also how it is being conveyed – whether positively, negatively, or neutrally. This information can help policymakers gauge public sentiment towards specific policy options more accurately and tailor their decisions accordingly. Sentiment analysis can also reveal underlying emotions that influence individuals' viewpoints, offering a more holistic understanding of public opinion beyond just factual statements.