English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper we present an application of the specific neural network model for sentiment attitude extraction without implementing handcrafted features. Given a mass-media article with the list of named entities mentioned in it, the task is to extract sentiment relations between these entities. We consider this problem as a three-class machine learning task for the whole documents. The modified architecture of the Convolutional Neural Networks was used and called as Piecewise Convolutional Neural Network (PCNN). The latter exploits positions of named entities in texts to emphasize aspects for inner and outer contexts of extracted relation. For the experiments, the RuSentRel corpus was used, it contains Russian analytical texts in the domain of international relations.

Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts