# ECO v1: Towards Event-Centric Opinion Mining

arXiv: https://arxiv.org/abs/2203.12264 · HTML: https://mlsanity.com/paper/2203.12264/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [Machine Comprehension Using Match-LSTM and Answer Pointer](https://mlsanity.com/paper/1608.07905/) (2016)
- [Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets](https://mlsanity.com/paper/1911.09241/) (2019)
- [Opinion mining of movie reviews at document level](https://mlsanity.com/paper/1408.3829/) (2014)

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

- [DLUE: Benchmarking Document Language Understanding](https://mlsanity.com/paper/2305.09520/) (2023)

## Abstract (arXiv)

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies.