Object coreference resolution is used in sentiment analysis to identify sentiment words referring to an aspect of an object in a document. However, this poses a challenge in natural language processing and is consequently an area of ongoing research. Further, to the best of our knowledge, object coreference resolution with more than one object has not been given much attention. To effectively address object coreference resolution, this paper proposes a method in which machine learning is applied to a large volume of textual data represented by context vectors, constituting a new form of language representation. The proposed machine learning model uses these vectors to achieve state-of-the-art performance in object coreference resolution. In addition, a combination of dependency grammar, sentiment ontology, and coreference graphs is used to obtain triplets of object, aspect, and sentiment. In experiments conducted on sentiment textual data obtained from Amazon.com, the proposed method achieved an average coreference resolution of object, aspect, and sentiment precision value of approximately 90%. This result suggests that the proposed method can contribute considerably to the field of object coreference resolution, and further research is therefore warranted.

对象共指解析在情感分析中用于识别引用文档中对象某个方面的情感词。然而，这在自然语言处理中提出了挑战，因此是正在进行的研究领域。此外，就我们所知，具有多个对象的对象共指解析尚未得到足够的重视。为了有效地解决对象共指分解问题，本文提出了一种将机器学习应用于由上下文向量表示的大量文本数据的方法，从而构成了一种新的语言表示形式。拟议的机器学习模型使用这些向量来实现对象共指分辨率方面的最新性能。此外，依存语法，情感本体，和共参考图用于获取对象，方面和情感的三元组。在对从Amazon.com获得的情感文本数据进行的实验中，提出的方法实现了对象，方面和情感精度的平均共参考分辨率，约为90％。该结果表明，所提出的方法可以在对象共指分辨率领域做出巨大贡献，因此有必要进行进一步的研究。