# Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis

arXiv: https://arxiv.org/abs/2401.06210 · HTML: https://mlsanity.com/paper/2401.06210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Word Mover's Embedding: From Word2Vec to Document Embedding](https://mlsanity.com/paper/1811.01713/) (2018)
- [Efficient Vector Representation for Documents through Corruption](https://mlsanity.com/paper/1707.02377/) (2017)

## Abstract (arXiv)

Document representation is the core of many NLP tasks on machine understanding. A general representation learned in an unsupervised manner reserves generality and can be used for various applications. In practice, sentiment analysis (SA) has been a challenging task that is regarded to be deeply semantic-related and is often used to assess general representations. Existing methods on unsupervised document representation learning can be separated into two families: sequential ones, which explicitly take the ordering of words into consideration, and non-sequential ones, which do not explicitly do so. However, both of them suffer from their own weaknesses. In this paper, we propose a model that overcomes difficulties encountered by both families of methods. Experiments show that our model outperforms state-of-the-art methods on popular SA datasets and a fine-grained aspect-based SA by a large margin.