# Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings

arXiv: https://arxiv.org/abs/1909.04917 · HTML: https://mlsanity.com/paper/1909.04917/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [ATESA-BAERT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2307.15920/) (2023)
- [Advancing Aspect-Based Sentiment Analysis through Deep Learning Models](https://mlsanity.com/paper/2404.03259/) (2024)
- [Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2503.12803/) (2025)
- [PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore](https://mlsanity.com/paper/2505.14165/) (2025)

## Abstract (arXiv)

Recently, a variety of model designs and methods have blossomed in the context of the sentiment analysis domain. However, there is still a lack of wide and comprehensive studies of aspect-based sentiment analysis (ABSA). We want to fill this gap and propose a comparison with ablation analysis of aspect term extraction using various text embedding methods. We particularly focused on architectures based on long short-term memory (LSTM) with optional conditional random field (CRF) enhancement using different pre-trained word embeddings. Moreover, we analyzed the influence on the performance of extending the word vectorization step with character embedding. The experimental results on SemEval datasets revealed that not only does bi-directional long short-term memory (BiLSTM) outperform regular LSTM, but also word embedding coverage and its source highly affect aspect detection performance. An additional CRF layer consistently improves the results as well.