Abstract
This paper considers the problem of semantically typing pet products using only independent and crowdsourced reviews provided for them on e-commerce websites by customers purchasing the product, rather than detailed product descriptions. Instead of proposing new methods, we consider the feasibility of established text classification algorithms in support of this goal. We conduct a detailed series of experiments, using three different methodologies and a two-level pet product taxonomy. Our results show that classic methods can serve as robust solutions to this problem, and that, while promising when more data is available, language models and word embeddings tend both to be more computationally intensive, as well as being susceptible to degraded performance in the long tail.
These authors contributed equally to this work.
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Notes
- 1.
- 2.
https://huggingface.co/datasets/amazon_us_reviews/viewer/Pet_Products_v1_00/train; Accessed: Jan 25, 2022.
- 3.
We could have also done a ‘global’ 80-20 split of the entire manually labeled sample of 1000 products, but this would not have guaranteed that all Level 1 categories were, in fact, represented. This is especially the case due to the ‘long-tail’ nature of the distribution: Level 1 categories like Reptile are significantly less prevalent in the sample and in the overall dataset than Dog or Cat.
- 4.
https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html; Accessed: March 1, 2023.
- 5.
https://docs.python.org/3/library/string.html; Accessed: March 1, 2023.
- 6.
https://scikit-learn.org/stable/modules/feature_extraction.html#stop-words; Accessed: March 1, 2023.
- 7.
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html; Accessed: March 1, 2023.
- 8.
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html; Accessed: March 1, 2023.
- 9.
https://www.nltk.org/api/nltk.tokenize.html; Accessed: March 1, 2023.
- 10.
Specifically, “fasttext-wiki-news-subwords-300” accessed at https://fasttext.cc/docs/en/english-vectors.html.
- 11.
https://nlp.stanford.edu/projects/glove/; Accessed: March 1, 2023.
- 12.
Specifically, the distilbert-base-uncased model accessed at https://huggingface.co/distilbert-base-uncased.
- 13.
Note that, because we have partitioned products into training and test sets, reviews would not ‘straddle’ the two sets: either all n reviews for a product would be allocated to the training set partition, or to the test set partition.
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Liu, X. et al. (2023). Automatic Semantic Typing of Pet E-commerce Products Using Crowdsourced Reviews: An Experimental Study. In: Ortiz-Rodriguez, F., Villazón-Terrazas, B., Tiwari, S., Bobed, C. (eds) Knowledge Graphs and Semantic Web. KGSWC 2023. Lecture Notes in Computer Science, vol 14382. Springer, Cham. https://doi.org/10.1007/978-3-031-47745-4_12
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