Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis

TL;DR

This paper introduces a novel method combining rhetorical analysis and sentiment analysis to extract aspects from opinions, generate summaries, and evaluate aspect importance, achieving high accuracy in aspect detection.

Contribution

The paper presents a new approach integrating Rhetorical Structure Theory with sentiment analysis for aspect extraction and summarization, including the use of aspect-aspect graphs for importance filtering.

Findings

High accuracy in aspect detection on standard datasets

Effective summarization of opinion sets

Prototype demonstrates practical viability

Abstract

This paper fills a gap in aspect-based sentiment analysis and aims to present a new method for preparing and analysing texts concerning opinion and generating user-friendly descriptive reports in natural language. We present a comprehensive set of techniques derived from Rhetorical Structure Theory and sentiment analysis to extract aspects from textual opinions and then build an abstractive summary of a set of opinions. Moreover, we propose aspect-aspect graphs to evaluate the importance of aspects and to filter out unimportant ones from the summary. Additionally, the paper presents a prototype solution of data flow with interesting and valuable results. The proposed method's results proved the high accuracy of aspect detection when applied to the gold standard dataset.