Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis
This work addresses the problem of generating user-friendly summaries from textual opinions for users in sentiment analysis, though it appears incremental as it builds on existing theories and techniques.
The paper tackled aspect-based sentiment analysis by developing a method using Rhetorical Structure Theory and sentiment analysis to extract aspects and generate abstractive summaries, achieving high accuracy in aspect detection on a gold standard dataset.
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.