# A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection

arXiv: https://arxiv.org/abs/1606.05704 · HTML: https://mlsanity.com/paper/1606.05704/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2016.

## Built on by (most cited first, 7 of 7)

- [I Beg to Differ: A study of constructive disagreement in online conversations](https://mlsanity.com/paper/2101.10917/) (2021)
- [How Did We Get Here? Summarizing Conversation Dynamics](https://mlsanity.com/paper/2404.19007/) (2024)
- [E-Commerce Dispute Resolution Prediction](https://mlsanity.com/paper/2110.15730/) (2021)
- [Conversation Derailment Forecasting with Graph Convolutional Networks](https://mlsanity.com/paper/2306.12982/) (2023)
- [A Similarity Measure for Comparing Conversational Dynamics](https://mlsanity.com/paper/2507.18956/) (2025)
- [Sentiment Progression based Searching and Indexing of Literary Textual Artefacts](https://mlsanity.com/paper/2106.13767/) (2021)
- [The geometry of online conversations and the causal antecedents of conflictual discourse](https://mlsanity.com/paper/2602.15600/) (2026)

## Abstract (arXiv)

We investigate the novel task of online dispute detection and propose a sentiment analysis solution to the problem: we aim to identify the sequence of sentence-level sentiments expressed during a discussion and to use them as features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the discussion as a whole. We evaluate dispute detection approaches on a newly created corpus of Wikipedia Talk page disputes and find that classifiers that rely on our sentiment tagging features outperform those that do not. The best model achieves a very promising F1 score of 0.78 and an accuracy of 0.80.