This research explores the possibility of improving knowledge-driven aspect-based sentiment analysis (ABSA) in terms of efficiency and effectiveness. This is done by implementing a Semi-automated Ontology Builder for Aspect-based sentiment analysis (SOBA). Semi-automatization of the ontology building process could produce more extensive ontologies, whilst shortening the building time. Furthermore, SOBA aims to improve the effectiveness of its ontologies in ABSA by attaching to concepts the semantics provided by a semantic lexicon. To evaluate the performance of SOBA, ontologies are created using the ontology builder for the restaurant and laptop domains. The use of these ontologies is then compared with the use of manually constructed ontologies in a state-of-the-art knowledge-driven ABSA model, the Two-Stage Hybrid Model (TSHM). The results show that it is difficult for a machine to beat the quality of a human made ontology, as SOBA does not improve the effectiveness of TSHM, achieving similar results. Including the semantics provided by a semantic lexicon in general increases the performance of TSHM, albeit not significantly. However, SOBA decreases by 50% or more the human time needed to build ontologies, so that it is recommended to use SOBA for knowledge-driven ABSA frameworks, as it leads to greater efficiency.

这项研究探索了在效率和有效性方面改善知识驱动的基于方面的情感分析（ABSA）的可能性。这是通过实现用于基于方面的情感分析（SOBA）的半自动本体构建器来完成的。本体构建过程的半自动化可以产生更广泛的本体，同时缩短构建时间。此外，SOBA旨在通过将语义词典提供的语义附加到概念上来提高其在ABSA中的本体的有效性。为了评估SOBA的性能，使用用于餐厅和笔记本电脑域的本体构建器来创建本体。然后，在最新的知识驱动型ABSA模型（两阶段混合模型（TSHM））中，将这些本体的使用与手动构建的本体的使用进行比较。结果表明，由于SOBA不能提高TSHM的有效性，因此机器很难击败人造本体的质量，从而获得相似的结果。通常，包括语义词典提供的语义可以提高TSHM的性能，尽管效果不明显。但是，SOBA将构建本体所需的人力减少了50％或更多的时间，因此建议将SOBA用于知识驱动的ABSA框架，因为这样可以提高效率。