mlsanityData to 7 Oct 2026

Zero-shot hashtag segmentation for multilingual sentiment analysis

arXiv 2112.03213 ↗First posted 6 Dec 2021cs.CLcited by 3

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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.

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

10 of 28 references.

References · 10

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  3. 2019Is Word Segmentation Necessary for Deep Learning of Chinese Representations?cited by 18
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  10. 2021BoostNSift: A Query Boosting and Code Sifting Technique for Method Level Bug Localizationcited by 3

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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

3 papers.