Predicting Readers' Emotional States Induced by News Articles through Latent Semantic Analysis

Diana Lupan (1) , Stefan Bobocescu-Kesikis (1) , Mihai Dascalu (1, 2) , Stefan Trausan-Matu (1) , Philippe Dessus (2)

1 Université Politehnica
2 LSE - Laboratoire des Sciences de l'Éducation (Grenoble)

With the increasing spread of the social web, identifying emotions in texts has proved to have various applications in fields like opinion mining or market analysis. Emotion recognition from written statements does not only reveal information about the person who wrote them, but can also be used in predicting how the emotional state of the readers can be affected. We propose a novel automatic method for analyzing texts that predicts how reading a news article can influence in turn the emotional state of the reader. This method integrates several word-count approaches and natural language processing techniques, such as Latent Semantic Analysis. Moreover, our implemented system contains a module designed to personalize the provided feedback according to the reader's current emotional state. A preliminary validation has been performed and results are promising.

Mots clés: automatic evaluation of news articles; emotional state; Latent Semantic Analysis

Domaines: Education; Environnements Informatiques pour l'Apprentissage Humain

Diana Lupan, Stefan Bobocescu-Kesikis, Mihai Dascalu, Stefan Trausan-Matu, Philippe Dessus. Predicting Readers' Emotional States Induced by News Articles through Latent Semantic Analysis. 1st Int. Conf. Social Media in Academia: Research and Teaching (SMART 2013), 2013, Bacau, Romania. ⟨hal-01471170⟩