Recursive Neural Text Classification Using Discourse Tree Structure for Argumentation Mining and Sentiment Analysis Tasks

P. 90–101.

Chernyavskiy A., Ilvovsky D.

This paper considers sentiment classification of movie reviews and two argument mining tasks: verification of political statements and categorization of quotes from an Internet forum corresponding to argumentation (factual or emotional). In the case of the fact-checking problem, justifications can be used additionally in one of its sub-tasks. A strong model for solving these and similar problems still does not exist. It requires the style-based approach to achieve the best results. The proposed model effectively encodes parsed discourse trees due to the recursive neural network. The novel siamese model based on it is suggested to analyze discourse structures for the pairs of texts. In the paper, the comparison with state-of-the-art methods is given. Experiments illustrate that the proposed models are effective and reach the best results in the assigned tasks. The evaluation also demonstrates that discourse analysis improves quality for the classification of longer texts.

Keywords: sentiment analysis, argument mining, fact-checking, discourse tree structure, recursive neural network, siamese model

Publication based on the results of: Intelligent Data Analysis in Interactive Systems for Transdisciplinary Applications (2020)

In book

Foundations of Intelligent Systems. 25th International Symposium on Methodologies for Intelligent Systems: ISMIS 2020

Vol. 12117. , Springer, 2020.