Abstract Emotion detection in the natural language text has drawn the attention of several scientific communities as well as commercial/marketing companies: analyzing human feelings expressed in the opinions and feedback of web users helps understand general moods and support market strategies for product advertising and market predictions. This paper proposes a framework for emotion-based classification from social streams, such as Twitter, according to Plutchik's wheel of emotions. An entropy-based weighted version of the fuzzy c-means (FCM) clustering algorithm, called EwFCM, to classify the data collected from streams has been proposed, improved by a fuzzy entropy method for the FCM center cluster initialization. Experimental results show that the proposed framework provides high accuracy in the classification of tweets according to Plutchik's primary emotions; moreover, the framework also allows the detection of secondary emotions, which, as defined by Plutchik, are the combination of the primary emotions. Finally, a comparative analysis with a similar fuzzy clustering-based approach for emotion classification shows that EwFCM converges more quickly with better performance in terms of accuracy, precision, and runtime. Finally, a straightforward mapping between the computed clusters and the emotion-based classes allows the assessment of the classification quality, reporting coherent and consistent results.

Improving the emotion-based classification by exploiting the fuzzy entropy in FCM clustering

Senatore, Sabrina
2021-01-01

Abstract

Abstract Emotion detection in the natural language text has drawn the attention of several scientific communities as well as commercial/marketing companies: analyzing human feelings expressed in the opinions and feedback of web users helps understand general moods and support market strategies for product advertising and market predictions. This paper proposes a framework for emotion-based classification from social streams, such as Twitter, according to Plutchik's wheel of emotions. An entropy-based weighted version of the fuzzy c-means (FCM) clustering algorithm, called EwFCM, to classify the data collected from streams has been proposed, improved by a fuzzy entropy method for the FCM center cluster initialization. Experimental results show that the proposed framework provides high accuracy in the classification of tweets according to Plutchik's primary emotions; moreover, the framework also allows the detection of secondary emotions, which, as defined by Plutchik, are the combination of the primary emotions. Finally, a comparative analysis with a similar fuzzy clustering-based approach for emotion classification shows that EwFCM converges more quickly with better performance in terms of accuracy, precision, and runtime. Finally, a straightforward mapping between the computed clusters and the emotion-based classes allows the assessment of the classification quality, reporting coherent and consistent results.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4767575
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 7
social impact