Emotions from text

Citations Over TimeTop 10% of 2005 papers

Abstract

In addition to information, text contains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture. The goal is to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a naïve baseline and BOW approach for classification of emotional versus non-emotional contents, with some dependency on parameter tuning. We also discuss results for a tripartite model which covers emotional valence, as well as feature set alternations. In addition, we present plans for a more cognitively sound sequential model, taking into consideration a larger set of basic emotions.

Related Papers

• → Positive and negative contexts predict duration of pig vocalisations(2019)69 cited
• → Valence sound symbolism across language families: a comparison between Japanese and German(2022)14 cited
• → Handedness affects Emotional Valence Asymmetry(1989)17 cited
• → The role of emotional valence in learning novel abstract concepts(2020)2 cited
• → Spring break or heart break? Extending valence bias to emotional words(2020)2 cited