Human emotion analysis has always stimulated studies in different disciplines, such as Cognitive Sciences, Psychology, and thanks to the diffusion of the social media, it is attracting the interests of computer scientists too. Particularly, the growing popularity of Microblogging platforms, has generated large amounts of information which in turn represent an attractive source of data to be further subjected to opinion mining and sentiment analysis. In our research, we leverage the analysis performed on micro-blogging texts and postings in Albanian language, which enables the use of technologies to monitor and follow the feelings and perception of the people with respect to products, issues, events, etc. Our approach to emotion analysis tackles the problem of classifying a text fragment into a set of pre-defined emotion categories and therefore aims at detecting the emotional state of the writer conveyed through the text. In order to achieve this goal, we perform a comparative analysis between different classifiers, using deep learning and other classical machine learning classification algorithms. We also adopt a domestic stemming tool for Albanian language in order to preprocess the datasets used in a second round of experiments. Experimental evaluation shows that deep learning produces overall better results compared with the other methods in terms of classification accuracy. We present also other findings related to the length of the texts being processed and the impact on the classifiers’ accuracy.

User-emotion detection through sentence-based classification using deep learning: A case-study with microblogs in albanian

Loglisci, Corrado;Ceci, Michelangelo;Malerba, Donato
2018

Abstract

Human emotion analysis has always stimulated studies in different disciplines, such as Cognitive Sciences, Psychology, and thanks to the diffusion of the social media, it is attracting the interests of computer scientists too. Particularly, the growing popularity of Microblogging platforms, has generated large amounts of information which in turn represent an attractive source of data to be further subjected to opinion mining and sentiment analysis. In our research, we leverage the analysis performed on micro-blogging texts and postings in Albanian language, which enables the use of technologies to monitor and follow the feelings and perception of the people with respect to products, issues, events, etc. Our approach to emotion analysis tackles the problem of classifying a text fragment into a set of pre-defined emotion categories and therefore aims at detecting the emotional state of the writer conveyed through the text. In order to achieve this goal, we perform a comparative analysis between different classifiers, using deep learning and other classical machine learning classification algorithms. We also adopt a domestic stemming tool for Albanian language in order to preprocess the datasets used in a second round of experiments. Experimental evaluation shows that deep learning produces overall better results compared with the other methods in terms of classification accuracy. We present also other findings related to the length of the texts being processed and the impact on the classifiers’ accuracy.
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: http://hdl.handle.net/11586/225814
 Attenzione

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

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? ND
social impact