Comparative Analysis of Classical and Deep Learning-based Natural Language Processing for Prioritizing Customer Complaints Article Swipe
Jan Blümel
,
Mohamed I. Zaki
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.24251/hicss.2022.236
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.24251/hicss.2022.236
Recent advancements in natural language processing have been shown to be very effective for different text mining tasks and thus have provided the opportunity to enhance service research. To improve the customer service experience, this paper compares several natural language processing approaches in order to automatically prioritize incoming customer complaints for service agents. This can help companies to reduce customers’ friction and enable effective resource allocations. Our paper uses state- of-the-art feature engineering techniques (e.g., term frequency, TF-IDF and Word2Vec) to identify key words that could enable machine to prioritize complainers. We experimented with many classical machine learning classification algorithms, such as Random Forests, Support Vector Machines, Decision Trees and Logistic Regression, as well as with deep learning-based classifiers, such as convolutional neural networks, bidirectional long short-term memory, and the pre-trained language model BERT to compare the model performance. Our findings show that the pre-trained language model BERT and TF- IDF in combination with Logistic Regression yields the highest macro averaged F1-score across the multiple classes and is therefore most capable of predicting the priority group of incoming customer complaints.
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- Type
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- Language
- en
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- OA Status
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All OpenAlex metadata
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https://openalex.org/W4220678434Canonical identifier for this work in OpenAlex
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https://doi.org/10.24251/hicss.2022.236Digital Object Identifier
- Title
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Comparative Analysis of Classical and Deep Learning-based Natural Language Processing for Prioritizing Customer ComplaintsWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
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2022-01-01Full publication date if available
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Jan Blümel, Mohamed I. ZakiList of authors in order
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https://doi.org/10.24251/hicss.2022.236Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.24251/hicss.2022.236Direct OA link when available
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Computer science, Natural language processing, Artificial intelligence, Deep learning, Natural languageTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 1, 2023: 1, 2022: 3Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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