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.
This work is partially supported by RFBR grant N 16-29-09606.
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Notes
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We use the zero vector value in case of a word absence in \(E_w\).
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We treat the contexts in order of their appearance in the text.
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Rusnachenko, N., Loukachevitch, N. (2019). Neural Network Approach for Extracting Aggregated Opinions from Analytical Articles. In: Manolopoulos, Y., Stupnikov, S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2018. Communications in Computer and Information Science, vol 1003. Springer, Cham. https://doi.org/10.1007/978-3-030-23584-0_10
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