Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Needmining is the process of extracting customer needs from user-generated content by classifying it as either informative or uninformative regarding need content. Contemporary studies achieve this by utilizing machine learning. However, models found in the literature cannot be compared to each other because they use private data for training and testing. This study benchmarks all previously suggested needmining models including CNN, SVM, RNN, and RoBERTa. To ensure an unbiased comparison, this study samples and annotates a dataset of customer reviews for products from 4 different categories from amazon. Henceforth, the dataset is publicly available and serves as a gold-set for future needmining benchmarks. RoBERTa outperformed other classifiers and seems to be best suited for needmining. The relevance of this study is reinforced by the fact that this benchmark creates a different hierarchy between models than otherwise suggested by comparing the results of previous studies.
Recommended Citation
Stahlmann, Sven; Ettrich, Oliver; Kurka, Marco; and Schoder, Detlef, "What Do Customers Say About My Products? Benchmarking Machine Learning Models for Need Identification" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 3.
https://aisel.aisnet.org/hicss-56/dsm/data_analytics/3
What Do Customers Say About My Products? Benchmarking Machine Learning Models for Need Identification
Online
Needmining is the process of extracting customer needs from user-generated content by classifying it as either informative or uninformative regarding need content. Contemporary studies achieve this by utilizing machine learning. However, models found in the literature cannot be compared to each other because they use private data for training and testing. This study benchmarks all previously suggested needmining models including CNN, SVM, RNN, and RoBERTa. To ensure an unbiased comparison, this study samples and annotates a dataset of customer reviews for products from 4 different categories from amazon. Henceforth, the dataset is publicly available and serves as a gold-set for future needmining benchmarks. RoBERTa outperformed other classifiers and seems to be best suited for needmining. The relevance of this study is reinforced by the fact that this benchmark creates a different hierarchy between models than otherwise suggested by comparing the results of previous studies.
https://aisel.aisnet.org/hicss-56/dsm/data_analytics/3