# Contextual and Position-Aware Factorization Machines for Sentiment Classification

arXiv: https://arxiv.org/abs/1801.06172 · HTML: https://mlsanity.com/paper/1801.06172/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [Boosting Factorization Machines via Saliency-Guided Mixup](https://mlsanity.com/paper/2206.08661/) (2022)

## Abstract (arXiv)

While existing machine learning models have achieved great success for sentiment classification, they typically do not explicitly capture sentiment-oriented word interaction, which can lead to poor results for fine-grained analysis at the snippet level (a phrase or sentence). Factorization Machine provides a possible approach to learning element-wise interaction for recommender systems, but they are not directly applicable to our task due to the inability to model contexts and word sequences. In this work, we develop two Position-aware Factorization Machines which consider word interaction, context and position information. Such information is jointly encoded in a set of sentiment-oriented word interaction vectors. Compared to traditional word embeddings, SWI vectors explicitly capture sentiment-oriented word interaction and simplify the parameter learning. Experimental results show that while they have comparable performance with state-of-the-art methods for document-level classification, they benefit the snippet/sentence-level sentiment analysis.