# Text-based Emotion Aware Recommender

arXiv: https://arxiv.org/abs/2007.01455 · HTML: https://mlsanity.com/paper/2007.01455/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [The Application of Affective Measures in Text-based Emotion Aware Recommender Systems](https://mlsanity.com/paper/2305.04796/) (2023)
- [Using Scene and Semantic Features for Multi-modal Emotion Recognition](https://mlsanity.com/paper/2308.00228/) (2023)

## Abstract (arXiv)

We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.