# Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach

arXiv: https://arxiv.org/abs/2003.09986 · HTML: https://mlsanity.com/paper/2003.09986/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Hijacking Large Language Models via Adversarial In-Context Learning](https://mlsanity.com/paper/2311.09948/) (2023)
- [Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification](https://mlsanity.com/paper/2101.05624/) (2021)
- [PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN](https://mlsanity.com/paper/2502.12207/) (2025)

## Abstract (arXiv)

Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations. However, these approaches heavily rely on manual tagging of user reviews according to the predefined aspects as the input, a laborious and time-consuming process. Moreover, the underlying methods do not explain how and why the opposing aspect level polarities in a user review lead to the overall polarity. In this paper, we tackle these two problems by designing and implementing a new Multiple-Attention Network (MAN) approach for more powerful ABSA without the need for aspect tags using two new tag-free data sets crawled directly from TripAdvisor ({https://www.tripadvisor.com}). With the Self- and Position-Aware attention mechanism, MAN is capable of extracting both aspect level and overall sentiments from the text reviews using the aspect level and overall customer ratings, and it can also detect the vital aspect(s) leading to the overall sentiment polarity among different aspects via a new aspect ranking scheme. We carry out extensive experiments to demonstrate the strong performance of MAN compared to other state-of-the-art ABSA approaches and the explainability of our approach by visualizing and interpreting attention weights in case studies.