The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. In this paper, we present a cloud-based system that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector embedding-based keyword extraction, and clustering. The elements of our model have been integrated and further developed to meet better the requirements of efficient information extraction, topic modeling of the extracted information, and user needs. Furthermore, our system can achieve better results than this task's existing topic modeling and keyword extraction solutions. Our approach is validated and compared with other state-of-the-art methods using publicly available datasets for benchmarking.

翻译：自然语言处理效率随着机器学习模型（尤其是基于神经网络的解决方案）的出现而显著提升。然而，某些任务仍具挑战性，尤其在特定领域场景中。本文提出一种基于云计算的系统，通过集成机器学习方法的流水线从客户评价中提取洞察信息。在主题建模方面，我们的复合模型采用面向自然语言处理的Transformer神经网络、基于向量嵌入的关键词提取以及聚类算法。模型各组件经过整合与优化，以更好地满足高效信息提取、提取信息主题建模及用户需求。此外，本系统在该任务中相较于现有主题建模与关键词提取方案能够取得更优结果。通过使用公开数据集进行基准测试，我们的方法得到验证，并与当前最优方法进行了对比分析。