Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text. However, the output of these machine learning (ML) models cannot be natively interpreted. In this paper, we study how the sentiment polarity predictions by DNNs can be explained and compare them to humans’ explanations. We crowdsource a corpus of Personal Narratives and ask human judges to annotate them with polarity and select the corresponding token chunks - the Emotion Carriers (EC) - that convey narrators’ emotions in the text. The interpretations of ML neural models are carried out through Integrated Gradients method and we compare them with human annotators’ interpretations. The results of our comparative analysis indicate that while the ML model mostly focuses on the explicit appearance of emotions-laden words (e.g. happy, frustrated), the human annotator predominantly focuses the attention on the manifestation of emotions through ECs that denote events, persons, and objects which activate narrator’s emotional state.

Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives / Mousavi, M.; Roccabruna, G.; Tammewar, A.; Azzolin, Steve; Riccardi, G.. - (2022). (Intervento presentato al convegno Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis tenutosi a Dublin nel 23rd - 27th May 2022).

Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives

Mousavi M.;Roccabruna G.;Tammewar A.;Azzolin Steve;Riccardi G.
2022-01-01

Abstract

Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text. However, the output of these machine learning (ML) models cannot be natively interpreted. In this paper, we study how the sentiment polarity predictions by DNNs can be explained and compare them to humans’ explanations. We crowdsource a corpus of Personal Narratives and ask human judges to annotate them with polarity and select the corresponding token chunks - the Emotion Carriers (EC) - that convey narrators’ emotions in the text. The interpretations of ML neural models are carried out through Integrated Gradients method and we compare them with human annotators’ interpretations. The results of our comparative analysis indicate that while the ML model mostly focuses on the explicit appearance of emotions-laden words (e.g. happy, frustrated), the human annotator predominantly focuses the attention on the manifestation of emotions through ECs that denote events, persons, and objects which activate narrator’s emotional state.
2022
Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Dublin
ACL
Mousavi, M.; Roccabruna, G.; Tammewar, A.; Azzolin, Steve; Riccardi, G.
Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives / Mousavi, M.; Roccabruna, G.; Tammewar, A.; Azzolin, Steve; Riccardi, G.. - (2022). (Intervento presentato al convegno Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis tenutosi a Dublin nel 23rd - 27th May 2022).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/340460
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