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  • × author_ss:"Martín-Valdivia, M.T."
  • × year_i:[2010 TO 2020}
  1. Rushdi-Saleh, M.; Martín-Valdivia, M.T.; Ureña-López, L.A.; Perea-Ortega, J.M.: OCA: Opinion corpus for Arabic (2011) 0.04 
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    Abstract
    Sentiment analysis is a challenging new task related to text mining and natural language processing. Although there are, at present, several studies related to this theme, most of these focus mainly on English texts. The resources available for opinion mining (OM) in other languages are still limited. In this article, we present a new Arabic corpus for the OM task that has been made available to the scientific community for research purposes. The corpus contains 500 movie reviews collected from different web pages and blogs in Arabic, 250 of them considered as positive reviews, and the other 250 as negative opinions. Furthermore, different experiments have been carried out on this corpus, using machine learning algorithms such as support vector machines and Nave Bayes. The results obtained are very promising and we are encouraged to continue this line of research.
  2. Perea-Ortega, J.M.; Martín-Valdivia, M.T.; Ureña-López, L.A.; Martínez-Cámara, E.: Improving polarity classification of bilingual parallel corpora combining machine learning and semantic orientation approaches (2013) 0.02 
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    Abstract
    Polarity classification is one of the main tasks related to the opinion mining and sentiment analysis fields. The aim of this task is to classify opinions as positive or negative. There are two main approaches to carrying out polarity classification: machine learning and semantic orientation based on the integration of knowledge resources. In this study, we propose to combine both approaches using a voting system based on the majority rule. In this way, we attempt to improve the polarity classification of two parallel corpora such as the opinion corpus for Arabic (OCA) and the English version of the OCA (EVOCA). Several experiments have been performed to check the feasibility of the proposed method. The results show that the experiment that took into account both approaches in the voting system obtained the best performance. Moreover, it is also shown that the proposed method slightly improves the best results obtained using machine learning approaches solely over the OCA and the EVOCA separately. Therefore, we can conclude that the approach proposed here might be considered a good strategy for polarity detection when we work with bilingual parallel corpora.

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