# Distant supervision for emotion detection using Facebook reactions

arXiv: https://arxiv.org/abs/1611.02988 · HTML: https://mlsanity.com/paper/1611.02988/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2016.

## Built on by (most cited first, 8 of 8)

- [A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions](https://mlsanity.com/paper/2205.03235/) (2022)
- [Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts](https://mlsanity.com/paper/2112.00468/) (2021)
- [Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter](https://mlsanity.com/paper/2105.09967/) (2021)
- [Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data](https://mlsanity.com/paper/2201.03941/) (2022)
- [Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource Languages](https://mlsanity.com/paper/2210.14472/) (2022)
- [SocialNLP EmotionGIF 2020 Challenge Overview: Predicting Reaction GIF Categories on Social Media](https://mlsanity.com/paper/2102.12073/) (2021)
- ["That's so cute!": The CARE Dataset for Affective Response Detection](https://mlsanity.com/paper/2201.11895/) (2022)
- [Multimodal Sentiment Analysis: Perceived vs Induced Sentiments](https://mlsanity.com/paper/2312.07627/) (2023)

## Abstract (arXiv)

We exploit the Facebook reaction feature in a distant supervised fashion to train a support vector machine classifier for emotion detection, using several feature combinations and combining different Facebook pages. We test our models on existing benchmarks for emotion detection and show that employing only information that is derived completely automatically, thus without relying on any handcrafted lexicon as it's usually done, we can achieve competitive results. The results also show that there is large room for improvement, especially by gearing the collection of Facebook pages, with a view to the target domain.