# Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees

## TL;DR

This paper introduces a discourse-aware sentiment analysis method using rhetorical structure theory and deep neural networks, improving accuracy by leveraging discourse trees and providing interpretability.

## Contribution

It presents a novel tensor-based deep neural network (Discourse-LSTM) that processes discourse trees for sentiment analysis, incorporating data augmentation techniques to enhance performance.

## Key findings

- Outperforms traditional bag-of-words methods in sentiment accuracy
- Provides interpretability by highlighting salient discourse passages
- Demonstrates robustness with data augmentation algorithms

## Abstract

Prominent applications of sentiment analysis are countless, covering areas such as marketing, customer service and communication. The conventional bag-of-words approach for measuring sentiment merely counts term frequencies; however, it neglects the position of the terms within the discourse. As a remedy, we develop a discourse-aware method that builds upon the discourse structure of documents. For this purpose, we utilize rhetorical structure theory to label (sub-)clauses according to their hierarchical relationships and then assign polarity scores to individual leaves. To learn from the resulting rhetorical structure, we propose a tensor-based, tree-structured deep neural network (named Discourse-LSTM) in order to process the complete discourse tree. The underlying tensors infer the salient passages of narrative materials. In addition, we suggest two algorithms for data augmentation (node reordering and artificial leaf insertion) that increase our training set and reduce overfitting. Our benchmarks demonstrate the superior performance of our approach. Moreover, our tensor structure reveals the salient text passages and thereby provides explanatory insights.