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ADOPS: Aspect Discovery OPinion Summarisation Methodology based on deep learning and subgroup discovery for generating explainable opinion summaries - ScienceDirect
Source: gfbfha15cc938d2b446a9spkf6nb6c5nqo6uxw.fgac.kust.cwkeji.cn
Article summary:
1. This article presents a novel methodology for aspect-based opinion summarisation, combining deep learning and subgroup discovery methods.
2. The summaries are presented in explainable terms for humans as interesting rules.
3. A new dataset of opinions about a single entity on the restaurant review domain has been released to assess the ADOPS methodology.
Article analysis:
The article is generally reliable and trustworthy, as it provides an overview of a novel methodology for aspect-based opinion summarisation that combines deep learning and subgroup discovery methods. The authors have also released a new dataset of opinions about a single entity on the restaurant review domain to assess the ADOPS methodology, which adds to its credibility. Furthermore, the results show that ADOPS is able to generate interesting rules with high values of support and confidence, which provide explainable and insightful knowledge about the state of the opinion of a certain entity.
However, there are some potential biases in the article that should be noted. For instance, there is no mention of any possible risks associated with using this method or any counterarguments against it. Additionally, there is no discussion of how this method could be used in other domains or contexts beyond restaurant reviews. Finally, while the authors do provide an overview of existing sentiment analysis techniques such as ABSA and SD, they do not explore them in depth or discuss their limitations or drawbacks.
Topics for further research:
Risks associated with sentiment analysis
Limitations of aspect-based sentiment analysis
Counterarguments against deep learning for sentiment analysis
Applications of sentiment analysis in other domains
Explainable knowledge generation from sentiment analysis
Drawbacks of subgroup discovery methods for sentiment analysis