Publication Search Form




We found publication with these paramters.

Citizen Opinion Summarization for Electronic Governance by Harnessing the Crowd

William Darling, Guillaume Bouchard
To reach the ideals that a true democracy can achieve, citizens must be able to participate in the political decision making process as easily as possible. Soliciting opinions on the Internet is a start, hut even if the people heed the call, it is then left to the policy makers to make sense of the data that has been left, which will likely include redundancy. off-topic complaints, spam, and other noise. To deal with this problem, we present a system that intelligently summarizes citizens’ opinions in the political context and presents them to policy makers in a structured way such that the electorate can be understood in terms of their problems and their sentiment with respect to proposed and completed political decisions. We present a high-quality novel fully automatic summarization system by harnessing the “wisdom of the crowd”; while we make use of iupervmed machine learning algorithms, our training data is created in real-time by calling crowd-sourcing platforms that interactively build up our classifiers. In this paper, we describe our system as a four step process: finding opinion bearing comments, learning the topics that they cover, classifying the sentiment that is reflected, and selecting representative sentences for the outputted structured summary We present our approach along with quantitative and qualitative experimental results and example summaries on a notice and comment dataset for the USDA’s proposed National Organic Program regulations. Our results show that this method efficiently creates interpretable and useftil summaries that will be helpful in improving political participation.
Will appear on ACM Transactions on Knowledge Discovery from Data.