# SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets

arXiv: https://arxiv.org/abs/2008.04277 · HTML: https://mlsanity.com/paper/2008.04277/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 30 of 62)

- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter](https://mlsanity.com/paper/2209.07562/) (2022)
- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text](https://mlsanity.com/paper/2107.03760/) (2021)
- [PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection](https://mlsanity.com/paper/2210.03221/) (2022)
- [Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots](https://mlsanity.com/paper/2103.09593/) (2021)
- [RoBERTuito: a pre-trained language model for social media text in Spanish](https://mlsanity.com/paper/2111.09453/) (2021)
- [Can You Traducir This? Machine Translation for Code-Switched Input](https://mlsanity.com/paper/2105.04846/) (2021)
- [DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text](https://mlsanity.com/paper/2106.09460/) (2021)
- [Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Text](https://mlsanity.com/paper/2106.10123/) (2021)
- [HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition](https://mlsanity.com/paper/2102.01909/) (2021)
- [Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes](https://mlsanity.com/paper/2303.09892/) (2023)
- [SJ_AJ@DravidianLangTech-EACL2021: Task-Adaptive Pre-Training of Multilingual BERT models for Offensive Language Identification](https://mlsanity.com/paper/2102.01051/) (2021)
- [MINERS: Multilingual Language Models as Semantic Retrievers](https://mlsanity.com/paper/2406.07424/) (2024)
- [Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark](https://mlsanity.com/paper/2306.07902/) (2023)
- [Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text](https://mlsanity.com/paper/2506.14012/) (2025)
- [CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages](https://mlsanity.com/paper/2507.18791/) (2025)
- [CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing](https://mlsanity.com/paper/2106.06004/) (2021)
- [Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data](https://mlsanity.com/paper/2103.14797/) (2021)
- [MIPE: A Metric Independent Pipeline for Effective Code-Mixed NLG Evaluation](https://mlsanity.com/paper/2107.11534/) (2021)
- [Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text](https://mlsanity.com/paper/2108.01861/) (2021)
- [Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text](https://mlsanity.com/paper/2403.04872/) (2024)
- [Sentiment Analysis of Persian-English Code-mixed Texts](https://mlsanity.com/paper/2102.12700/) (2021)
- [Offense Detection in Dravidian Languages using Code-Mixing Index based Focal Loss](https://mlsanity.com/paper/2111.06916/) (2021)
- [Task-Specific Pre-Training and Cross Lingual Transfer for Code-Switched Data](https://mlsanity.com/paper/2102.12407/) (2021)
- [Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation](https://mlsanity.com/paper/2403.16771/) (2024)
- [CHAI for LLMs: Improving Code-Mixed Translation in Large Language Models through Reinforcement Learning with AI Feedback](https://mlsanity.com/paper/2411.09073/) (2024)
- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [Challenges and Considerations with Code-Mixed NLP for Multilingual Societies](https://mlsanity.com/paper/2106.07823/) (2021)

## Abstract (arXiv)

In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our Hinglish (Hindi-English) and Spanglish (Spanish-English) corpora annotated with word-level language identification and sentence-level sentiment labels. These corpora are comprised of 20K and 19K examples, respectively. The sentiment labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions in total including 61 teams that participated in the Hinglish contest and 28 submitted systems to the Spanglish competition. The best performance achieved was 75.0% F1 score for Hinglish and 80.6% F1 for Spanglish. We observe that BERT-like models and ensemble methods are the most common and successful approaches among the participants.