Assessing Weight of Opinion by Aggregating Coalitions of Arguments
Pavithra Rajendran, Danushka Bollegala, Simon Parsons · Frontiers in artificial intelligence and applications · 2016
Argument mining promises to be able to extract information from unstructured text that can help us to understand that text. This paper suggests a novel way to use such information once it has been extracted. Attack and support relations between arguments from a set of test texts are identified, the strength of the arguments is computed based on the relations, and arguments are grouped into coalitions. The resulting set of arguments is then used to predict the weight of opinion in new text, by identifying arguments whose weight has been computed, and aggregating these weights. Our approach is evaluated on a corpus of hotel reviews, and compared with an existing method of predicting the sentiment of reviews.