# Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis

arXiv: https://arxiv.org/abs/2212.08888 · HTML: https://mlsanity.com/paper/2212.08888/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 17 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Improving Review Representations with User Attention and Product Attention for Sentiment Classification](https://mlsanity.com/paper/1801.07861/) (2018)
- [Rethinking Attribute Representation and Injection for Sentiment Classification](https://mlsanity.com/paper/1908.09590/) (2019)
- [Improving Document-Level Sentiment Analysis with User and Product Context](https://mlsanity.com/paper/2011.09210/) (2020)
- [Cold-Start Aware User and Product Attention for Sentiment Classification](https://mlsanity.com/paper/1806.05507/) (2018)
- [Dual Memory Network Model for Biased Product Review Classification](https://mlsanity.com/paper/1809.05807/) (2018)

## Abstract (arXiv)

User and product information associated with a review is useful for sentiment polarity prediction. Typical approaches incorporating such information focus on modeling users and products as implicitly learned representation vectors. Most do not exploit the potential of historical reviews, or those that currently do require unnecessary modifications to model architecture or do not make full use of user/product associations. The contribution of this work is twofold: i) a method to explicitly employ historical reviews belonging to the same user/product to initialize representations, and ii) efficient incorporation of textual associations between users and products via a user-product cross-context module. Experiments on IMDb, Yelp-2013 and Yelp-2014 benchmarks show that our approach substantially outperforms previous state-of-the-art. Since we employ BERT-base as the encoder, we additionally provide experiments in which our approach performs well with Span-BERT and Longformer. Furthermore, experiments where the reviews of each user/product in the training data are downsampled demonstrate the effectiveness of our approach under a low-resource setting.