Rafae Abderrahim, Erritali Mohammed, Madani Youness, Roche Mathieu.
2023. Using features based on elongation to enhance sentiment analysis.
In : Information management and big data. Lossio-Ventura Juan Antonio (ed.), Valverde-Rebaza Jorge (ed.), Díaz Eduardo (ed.) , Alatrista-Salas Hugo (ed.)
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Version publiée
- Anglais
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Résumé : Elongated words such as “heellloo” or “heyyy” are a frequent feature of oral communication and are often used to underline or exaggerate the hidden message of the root word. Although elongated words are rarely found in written languages and dictionaries, they are common in social networks. They can be considered in the analysis of users' sentiments. In this paper, we analyze the impact of elongation on the classification of sentiments, in addition to an in-depth study at the level of lexical forms of elongation. In this work we present a method to improve the accuracy of sentiment classification based on elongations of features. Experimental results conducted on Twitter data show that our model achieves an accuracy of 0.79 in 7-fold cross-validation experiments.
Mots-clés libres : Text Mining, Natural Language Processing, Data science, Sentiment analysis, Elongations, Textual features
Agences de financement européennes : European Commission
Projets sur financement : (EU) MOnitoring Outbreak events for Disease surveillance in a data science context
Auteurs et affiliations
- Rafae Abderrahim, Sultan Moulay Slimane University (MAR)
- Erritali Mohammed, Sultan Moulay Slimane University (MAR)
- Madani Youness, Sultan Moulay Slimane University (MAR)
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Roche Mathieu, CIRAD-ES-UMR TETIS (FRA)
ORCID: 0000-0003-3272-8568
Source : Cirad-Agritrop (https://agritrop.cirad.fr/610865/)
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