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Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts

10 pagesPublished: March 18, 2019

Abstract

In this paper we present an application of the specific neural network model for sentiment attitude extraction without handcrafted NLP features implementation. 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 considered this problem for the whole documents as a three-class machine learning task. The modified architecture of the Convolutional Neural Networks were used and called as Piecewise Convolutional Neural Network (PCNN). The latter exploits positions of named entities in text to emphasize aspects for inner and outer contexts of relation between entities. For the experiments, the RuSentRel corpus was used, it contains Russian analytical texts in the domain of international relations.

Keyphrases: coherent texts, convolutional neural networks, sentiment analysis

In: Gerhard Wohlgenannt, Ruprecht von Waldenfels, Svetlana Toldova, Ekaterina Rakhilina, Denis Paperno, Olga Lyashevskaya, Natalia Loukachevitch, Sergei O. Kuznetsov, Olga Kultepina, Dmitry Ilvovsky, Boris Galitsky, Ekaterina Artemova and Elena Bolshakova (editors). Proceedings of Third Workshop "Computational linguistics and language science", vol 4, pages 1-10.

BibTeX entry
@inproceedings{CLLS2018:Using_Convolutional_Neural_Networks,
  author    = {Nicolay Rusnachenko and Natalia Loukachevitch},
  title     = {Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts},
  booktitle = {Proceedings of Third Workshop "Computational linguistics and language science"},
  editor    = {Gerhard Wohlgenannt and Ruprecht von Waldenfels and Svetlana Toldova and Ekaterina Rakhilina and Denis Paperno and Olga Lyashevskaya and Natalia Loukachevitch and Sergei O. Kuznetsov and Olga Kultepina and Dmitry Ilvovsky and Boris Galitsky and Ekaterina Artemova and Elena Bolshakova},
  series    = {EPiC Series in Language and Linguistics},
  volume    = {4},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5283},
  url       = {/publications/paper/pQrC},
  doi       = {10.29007/26g7},
  pages     = {1-10},
  year      = {2019}}
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