mteb/tweet_sentiment_multilingual

TweetSentimentClassification

An MTEB dataset

Massive Text Embedding Benchmark

A multilingual Sentiment Analysis dataset consisting of tweets in 8 different languages.

Task category	t2c
Domains	Social, Written
Reference	https://aclanthology.org/2022.lrec-1.27

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_tasks(["TweetSentimentClassification"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repitory.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.

@inproceedings{barbieri-etal-2022-xlm,
  abstract = {Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.},
  address = {Marseille, France},
  author = {Barbieri, Francesco  and
Espinosa Anke, Luis  and
Camacho-Collados, Jose},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  month = jun,
  pages = {258--266},
  publisher = {European Language Resources Association},
  title = {{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond},
  url = {https://aclanthology.org/2022.lrec-1.27},
  year = {2022},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("TweetSentimentClassification")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 2048,
        "number_of_characters": 169117,
        "number_texts_intersect_with_train": 0,
        "min_text_length": 4,
        "average_text_length": 82.57666015625,
        "max_text_length": 200,
        "unique_text": 2048,
        "unique_labels": 3,
        "labels": {
            "1": {
                "count": 688
            },
            "2": {
                "count": 680
            },
            "0": {
                "count": 680
            }
        },
        "hf_subset_descriptive_stats": {
            "arabic": {
                "num_samples": 256,
                "number_of_characters": 21637,
                "number_texts_intersect_with_train": 0,
                "min_text_length": 14,
                "average_text_length": 84.51953125,
                "max_text_length": 140,
                "unique_text": 256,
                "unique_labels": 3,
                "labels": {
                    "1": {
                        "count": 86
                    },
                    "2": {
                        "count": 85
                    },
                    "0": {
                        "count": 85
                    }
                }
            },
            "english": {
                "num_samples": 256,
                "number_of_characters": 23508,
                "number_texts_intersect_with_train": 0,
                "min_text_length": 17,
                "average_text_length": 91.828125,
                "max_text_length": 141,
                "unique_text": 256,
                "unique_labels": 3,
                "labels": {
                    "1": {
                        "count": 86
                    },
                    "2": {
                        "count": 85
                    },
                    "0": {
                        "count": 85
                    }
                }
            },
            "german": {
                "num_samples": 256,
                "number_of_characters": 19069,
                "number_texts_intersect_with_train": 0,
                "min_text_length": 9,
                "average_text_length": 74.48828125,
                "max_text_length": 142,
                "unique_text": 256,
                "unique_labels": 3,
                "labels": {
                    "1": {
                        "count": 86
                    },
                    "2": {
                        "count": 85
                    },
                    "0": {
                        "count": 85
                    }
                }
            },
            "french": {
                "num_samples": 256,
                "number_of_characters": 24130,
                "number_texts_intersect_with_train": 0,
                "min_text_length": 23,
                "average_text_length": 94.2578125,
                "max_text_length": 140,
                "unique_text": 256,
                "unique_labels": 3,
                "labels": {
                    "1": {
                        "count": 86
                    },
                    "2": {
                        "count": 85
                    },
                    "0": {
                        "count": 85
                    }
                }
            },
            "italian": {
                "num_samples": 256,
                "number_of_characters": 23564,
                "number_texts_intersect_with_train": 0,
                "min_text_length": 14,
                "average_text_length": 92.046875,
                "max_text_length": 140,
                "unique_text": 256,
                "unique_labels": 3,
                "labels": {
                    "1": {
                        "count": 86
                    },
                    "2": {
                        "count": 85
                    },
                    "0": {
                        "count": 85
                    }
                }
            },
            "portuguese": {
                "num_samples": 256,
                "number_of_characters": 18522,
                "number_texts_intersect_with_train": 0,
                "min_text_length": 24,
                "average_text_length": 72.3515625,
                "max_text_length": 140,
                "unique_text": 256,
                "unique_labels": 3,
                "labels": {
                    "1": {
                        "count": 86
                    },
                    "2": {
                        "count": 85
                    },
                    "0": {
                        "count": 85
                    }
                }
            },
            "spanish": {
                "num_samples": 256,
                "number_of_characters": 21014,
                "number_texts_intersect_with_train": 0,
                "min_text_length": 22,
                "average_text_length": 82.0859375,
                "max_text_length": 137,
                "unique_text": 256,
                "unique_labels": 3,
                "labels": {
                    "1": {
                        "count": 86
                    },
                    "2": {
                        "count": 85
                    },
                    "0": {
                        "count": 85
                    }
                }
            },
            "hindi": {
                "num_samples": 256,
                "number_of_characters": 17673,
                "number_texts_intersect_with_train": 0,
                "min_text_length": 4,
                "average_text_length": 69.03515625,
                "max_text_length": 200,
                "unique_text": 256,
                "unique_labels": 3,
                "labels": {
                    "1": {
                        "count": 86
                    },
                    "2": {
                        "count": 85
                    },
                    "0": {
                        "count": 85
                    }
                }
            }
        }
    },
    "train": {
        "num_samples": 14712,
        "number_of_characters": 1277720,
        "number_texts_intersect_with_train": null,
        "min_text_length": 2,
        "average_text_length": 86.84883088635128,
        "max_text_length": 1085,
        "unique_text": 14712,
        "unique_labels": 3,
        "labels": {
            "0": {
                "count": 4904
            },
            "1": {
                "count": 4904
            },
            "2": {
                "count": 4904
            }
        },
        "hf_subset_descriptive_stats": {
            "arabic": {
                "num_samples": 1839,
                "number_of_characters": 164305,
                "number_texts_intersect_with_train": null,
                "min_text_length": 11,
                "average_text_length": 89.34475258292551,
                "max_text_length": 140,
                "unique_text": 1839,
                "unique_labels": 3,
                "labels": {
                    "0": {
                        "count": 613
                    },
                    "1": {
                        "count": 613
                    },
                    "2": {
                        "count": 613
                    }
                }
            },
            "english": {
                "num_samples": 1839,
                "number_of_characters": 201493,
                "number_texts_intersect_with_train": null,
                "min_text_length": 29,
                "average_text_length": 109.56661228928766,
                "max_text_length": 185,
                "unique_text": 1839,
                "unique_labels": 3,
                "labels": {
                    "0": {
                        "count": 613
                    },
                    "1": {
                        "count": 613
                    },
                    "2": {
                        "count": 613
                    }
                }
            },
            "german": {
                "num_samples": 1839,
                "number_of_characters": 137071,
                "number_texts_intersect_with_train": null,
                "min_text_length": 7,
                "average_text_length": 74.53561718325177,
                "max_text_length": 144,
                "unique_text": 1839,
                "unique_labels": 3,
                "labels": {
                    "0": {
                        "count": 613
                    },
                    "1": {
                        "count": 613
                    },
                    "2": {
                        "count": 613
                    }
                }
            },
            "french": {
                "num_samples": 1839,
                "number_of_characters": 178091,
                "number_texts_intersect_with_train": null,
                "min_text_length": 16,
                "average_text_length": 96.84121805328984,
                "max_text_length": 144,
                "unique_text": 1839,
                "unique_labels": 3,
                "labels": {
                    "0": {
                        "count": 613
                    },
                    "1": {
                        "count": 613
                    },
                    "2": {
                        "count": 613
                    }
                }
            },
            "italian": {
                "num_samples": 1839,
                "number_of_characters": 165828,
                "number_texts_intersect_with_train": null,
                "min_text_length": 6,
                "average_text_length": 90.17292006525285,
                "max_text_length": 150,
                "unique_text": 1839,
                "unique_labels": 3,
                "labels": {
                    "0": {
                        "count": 613
                    },
                    "1": {
                        "count": 613
                    },
                    "2": {
                        "count": 613
                    }
                }
            },
            "portuguese": {
                "num_samples": 1839,
                "number_of_characters": 135761,
                "number_texts_intersect_with_train": null,
                "min_text_length": 18,
                "average_text_length": 73.82327351821642,
                "max_text_length": 146,
                "unique_text": 1839,
                "unique_labels": 3,
                "labels": {
                    "0": {
                        "count": 613
                    },
                    "1": {
                        "count": 613
                    },
                    "2": {
                        "count": 613
                    }
                }
            },
            "spanish": {
                "num_samples": 1839,
                "number_of_characters": 153354,
                "number_texts_intersect_with_train": null,
                "min_text_length": 19,
                "average_text_length": 83.38988580750407,
                "max_text_length": 138,
                "unique_text": 1839,
                "unique_labels": 3,
                "labels": {
                    "0": {
                        "count": 613
                    },
                    "1": {
                        "count": 613
                    },
                    "2": {
                        "count": 613
                    }
                }
            },
            "hindi": {
                "num_samples": 1839,
                "number_of_characters": 141817,
                "number_texts_intersect_with_train": null,
                "min_text_length": 2,
                "average_text_length": 77.11636759108211,
                "max_text_length": 1085,
                "unique_text": 1839,
                "unique_labels": 3,
                "labels": {
                    "0": {
                        "count": 613
                    },
                    "1": {
                        "count": 613
                    },
                    "2": {
                        "count": 613
                    }
                }
            }
        }
    }
}

---

This dataset card was automatically generated using MTEB