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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.07786 |
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| _version_ | 1866914710005219328 |
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| author | Lakatos, Robert Bogacsovics, Gergo Harangi, Balazs Lakatos, Istvan Tiba, Attila Toth, Janos Szabo, Marianna Hajdu, Andras |
| author_facet | Lakatos, Robert Bogacsovics, Gergo Harangi, Balazs Lakatos, Istvan Tiba, Attila Toth, Janos Szabo, Marianna Hajdu, Andras |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_07786 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews Lakatos, Robert Bogacsovics, Gergo Harangi, Balazs Lakatos, Istvan Tiba, Attila Toth, Janos Szabo, Marianna Hajdu, Andras Computation and Language Artificial Intelligence 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. |
| title | A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2306.07786 |