# Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites

arXiv: https://arxiv.org/abs/2207.05979 · HTML: https://mlsanity.com/paper/2207.05979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1904.02232/) (2019)

## Abstract (arXiv)

Consumers often read product reviews to inform their buying decision, as some consumers want to know a specific component of a product. However, because typical sentences on product reviews contain various details, users must identify sentences about components they want to know amongst the many reviews. Therefore, we aimed to develop a system that identifies and collects component and aspect information of products in sentences. Our BERT-based classifiers assign labels referring to components and aspects to sentences in reviews and extract sentences with comments on specific components and aspects. We determined proper labels based for the words identified through pattern matching from product reviews to create the training data. Because we could not use the words as labels, we carefully created labels covering the meanings of the words. However, the training data was imbalanced on component and aspect pairs. We introduced a data augmentation method using WordNet to reduce the bias. Our evaluation demonstrates that the system can determine labels for road bikes using pattern matching, covering more than 88% of the indicators of components and aspects on e-commerce sites. Moreover, our data augmentation method can improve the-F1-measure on insufficient data from 0.66 to 0.76.