Authors:
Iago C. Chaves
;
Antônio Diogo F. Martins
;
Francisco D. B. S. Praciano
;
Felipe T. Brito
;
Jose Maria Monteiro
and
Javam C. Machado
Affiliation:
Computer Science Department, Universidade Federal do Ceará, Fortaleza, Brazil
Keyword(s):
Natural Language Processing, Sentiment Analysis, LSTM; Pooling, Attention Mechanism.
Abstract:
Sentiment analysis (SA) is the automatic process of understanding people’s feelings or beliefs expressed in texts such as emotions, opinions, attitudes, appraisals and others. The main task is to identify the polarity level (positive, neutral or negative) of a given text. This task has been the subject of several research competitions in many languages, for instance, English, Spanish and Arabic. However, developing a multilingual sentiment analysis method remains a challenge. In this paper, we propose a new approach, called BPA, based on BiLSTM neural networks, pooling operations and attention mechanism, which is able to automatically classify the polarity level of a text. We evaluated the BPA approach using five different data sets in three distinct languages: English, Spanish and Portuguese. Experimental results evidence the suitability of the proposed approach to multilingual and domain-independent polarity classification. BPA’s best results achieved an accuracy of 0.901, 0.865 an
d 0.923 for English, Spanish and Portuguese, respectively.
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