• Corpus ID: 218613680

A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text

@article{Lipizzi2020ACM,
  title={A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text},
  author={Carlo Lipizzi and Dario Borrelli and Fernanda de Oliveira Capela},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.06059}
}
This work introduces a new method to consider subjectivity and general context dependency in text analysis and uses as example the detection of emotions conveyed in text. The proposed method takes into account subjectivity using a computational version of the Framework Theory by Marvin Minsky (1974) leveraging on the Word2Vec approach to text vectorization by Mikolov et al. (2013), used to generate distributed representation of words based on the context where they appear. Our approach is based… 

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References

SHOWING 1-10 OF 61 REFERENCES

A model of textual affect sensing using real-world knowledge

This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations to classify sentences into "basic" emotion categories, and suggests that the approach is robust enough to enable plausible affective text user interfaces.

Emotions from Text: Machine Learning for Text-based Emotion Prediction

This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture 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.

Recognizing Emotions in Text

This thesis explores approaches to automatic detection of emotions in text through studies and experiments in manual and automatic recognition of expressions of the six basic emotions – happiness, sadness, anger, disgust, surprise, and fear – in text form.

Text-based emotion classification using emotion cause extraction

The psychological foundations of the affective lexicon.

Subjects rated their confidence that each word from a set of 585 words referred to an emotion. As a strategy for discriminating words that refer to genuine emotions from words that refer to other

Distant Supervision for Emotion Classification with Discrete Binary Values

This paper treats not only emoticons and hashtags but also emoji, which are increasingly used in social media, as an alternative for explicit, manual labels, and develops a single multi-label classifier for Plutchik's eight emotions that achieves accuracies superior to those reported in previous multi-way classification studies.

Emotion Categories Across Languages

The Cognitive Structure of Emotions

The boundaries of the theory Emotion words and cross-cultural issues Emotion experiences and unconscious emotions Coping and the function of emotions Computational tractability.

Handbook of Categorization in Cognitive Science

SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS

It is shown that extending the term‐counting method with contextual valence shifters improves the accuracy of the classification, and combining the two methods achieves better results than either method alone.
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