Neural Network Approach for Extracting Aggregated Opinions from Analytical Articles
Authors: Rusnachenko, N. and Loukachevitch, N.
Journal: Communications in Computer and Information Science
Volume: 1003
Pages: 167-179
eISSN: 1865-0937
ISBN: 9783030235833
ISSN: 1865-0929
DOI: 10.1007/978-3-030-23584-0_10
Abstract:Large texts that analyze a situation in some domain, for example politics or economy, usually are full of opinions. In case of analytical articles, opinions usually are a kind of attitudes with source and target presented as named entities, both mentioned in the text. We present an application of the specific neural network model for sentiment attitude extraction. This problem is considered as a three-class machine learning task for the whole documents. Treating text attitudes as a list of related contexts, we first extract related sentiment contexts and then calculate the resulted attitude label. For sentiment context extraction, we use Piecewise Convolutional Neural Network (PCNN). We experiment with variety of functions that allows us to compose the attitude label, including recurrent neural network, which give the possibility to take into account additional context aspects. For experiments, the RuSentRel corpus was used, it contains Russian analytical texts in the domain of international relations.
Source: Scopus