Deep Learning for Sentiment Analysis of Tunisian Dialect

Abir Masmoudi, Jamila Hamdi, Lamia Hadrich Belguith


Automatic sentiment analysis has become one of the fastest growing research areas in the Natural Language Processing (NLP) field. Despite its importance, this is the first work towards sentiment analysis at both aspect and sentence levels for the Tunisian Dialect in the field of Tunisian supermarkets. Therefore, we experimentally evaluate, in this paper, three deep learning methods, namely convolution neural networks (CNN), long short-term memory (LSTM), and bi-directional long-short-term-memory (Bi-LSTM). Both LSTM and Bi-LSTM constitute two major types of Recurrent Neural Networks (RNN). Towards this end, we gathered a corpus containing comments posted on the official Facebook pages of Tunisian supermarkets. To conduct our experiments, this corpus was annotated on the basis of five criteria (very positive/ positive/ neutral/ negative/ very negative) and other twenty categories of aspects. In this evaluation, we show that the gathered features can lead to very encouraging performances through the use of CNN and Bi-LSTM neural networks.


Sentiment analysis, tunisian dialect, social networks, aspect-based sentiment analysis, sentence-based sentiment analysis, Big data, CNN, RNN

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