# Zero-shot hashtag segmentation for multilingual sentiment analysis

arXiv: https://arxiv.org/abs/2112.03213 · HTML: https://mlsanity.com/paper/2112.03213/

## Key facts

- Cited by 3 papers in the map (data through 7 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Is Word Segmentation Necessary for Deep Learning of Chinese Representations?](https://mlsanity.com/paper/1905.05526/) (2019)
- [Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning](https://mlsanity.com/paper/1903.04190/) (2019)
- [Universal Word Segmentation: Implementation and Interpretation](https://mlsanity.com/paper/1807.02974/) (2018)
- [RethinkCWS: Is Chinese Word Segmentation a Solved Task?](https://mlsanity.com/paper/2011.06858/) (2020)
- [Multi-task Pairwise Neural Ranking for Hashtag Segmentation](https://mlsanity.com/paper/1906.00790/) (2019)
- [Comparing Neural- and N-Gram-Based Language Models for Word Segmentation](https://mlsanity.com/paper/1812.00815/) (2018)
- [Unified Multi-Criteria Chinese Word Segmentation with BERT](https://mlsanity.com/paper/2004.05808/) (2020)
- [BoostNSift: A Query Boosting and Code Sifting Technique for Method Level Bug Localization](https://mlsanity.com/paper/2108.12901/) (2021)

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

- [HashSet -- A Dataset For Hashtag Segmentation](https://mlsanity.com/paper/2201.06741/) (2022)
- [HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding](https://mlsanity.com/paper/2308.09985/) (2023)
- [Hate Speech Detection in Turkish and Arabic: A Comprehensive Study](https://mlsanity.com/paper/2607.00143/) (2026)

## Abstract (arXiv)

Hashtag segmentation, also known as hashtag decomposition, is a common step in preprocessing pipelines for social media datasets. It usually precedes tasks such as sentiment analysis and hate speech detection. For sentiment analysis in medium to low-resourced languages, previous research has demonstrated that a multilingual approach that resorts to machine translation can be competitive or superior to previous approaches to the task. We develop a zero-shot hashtag segmentation framework and demonstrate how it can be used to improve the accuracy of multilingual sentiment analysis pipelines. Our zero-shot framework establishes a new state-of-the-art for hashtag segmentation datasets, surpassing even previous approaches that relied on feature engineering and language models trained on in-domain data.