# Building a Sentiment Corpus of Tweets in Brazilian Portuguese

arXiv: https://arxiv.org/abs/1712.08917 · HTML: https://mlsanity.com/paper/1712.08917/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2017.

## Built on by (most cited first, 20 of 20)

- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [TweetNLP: Cutting-Edge Natural Language Processing for Social Media](https://mlsanity.com/paper/2206.14774/) (2022)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [Cabrita: closing the gap for foreign languages](https://mlsanity.com/paper/2308.11878/) (2023)
- [An Efficient Multilingual Language Model Compression through Vocabulary Trimming](https://mlsanity.com/paper/2305.15020/) (2023)
- [Sabi'a: Portuguese Large Language Models](https://mlsanity.com/paper/2304.07880/) (2023)
- [BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams](https://mlsanity.com/paper/2307.05410/) (2023)
- [Tucano: Advancing Neural Text Generation for Portuguese](https://mlsanity.com/paper/2411.07854/) (2024)
- [Introducing Bode: A Fine-Tuned Large Language Model for Portuguese Prompt-Based Task](https://mlsanity.com/paper/2401.02909/) (2024)
- [Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark](https://mlsanity.com/paper/2306.07902/) (2023)
- [pysentimiento: A Python Toolkit for Opinion Mining and Social NLP tasks](https://mlsanity.com/paper/2106.09462/) (2021)
- [ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language](https://mlsanity.com/paper/2406.10806/) (2024)
- [Building High-Quality Datasets for Portuguese LLMs: From Common Crawl Snapshots to Industrial-Grade Corpora](https://mlsanity.com/paper/2509.08824/) (2025)
- [PoETa v2: Toward More Robust Evaluation of Large Language Models in Portuguese](https://mlsanity.com/paper/2511.17808/) (2025)
- [Assessment of Massively Multilingual Sentiment Classifiers](https://mlsanity.com/paper/2204.04937/) (2022)
- [The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages](https://mlsanity.com/paper/2310.14557/) (2023)
- [BRoverbs -- Measuring how much LLMs understand Portuguese proverbs](https://mlsanity.com/paper/2509.08960/) (2025)
- [A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets](https://mlsanity.com/paper/2404.00297/) (2024)
- [NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus](https://mlsanity.com/paper/2605.00086/) (2026)
- [Performance of Data Augmentation Methods for Brazilian Portuguese Text Classification](https://mlsanity.com/paper/2304.02785/) (2023)

## Abstract (arXiv)

The large amount of data available in social media, forums and websites motivates researches in several areas of Natural Language Processing, such as sentiment analysis. The popularity of the area due to its subjective and semantic characteristics motivates research on novel methods and approaches for classification. Hence, there is a high demand for datasets on different domains and different languages. This paper introduces TweetSentBR, a sentiment corpora for Brazilian Portuguese manually annotated with 15.000 sentences on TV show domain. The sentences were labeled in three classes (positive, neutral and negative) by seven annotators, following literature guidelines for ensuring reliability on the annotation. We also ran baseline experiments on polarity classification using three machine learning methods, reaching 80.99% on F-Measure and 82.06% on accuracy in binary classification, and 59.85% F-Measure and 64.62% on accuracy on three point classification.