Deep Learning for Sentiment Analysis of Tunisian Dialect



Título del documento: Deep Learning for Sentiment Analysis of Tunisian Dialect
Revista: Computación y sistemas
Base de datos:
Número de sistema: 000560568
ISSN: 1405-5546
Autores: 1
1
1
Instituciones: 1University of Sfax, MIRACL Laboratory, Sfax. Túnez
Año:
Periodo: Ene-Mar
Volumen: 25
Número: 1
Paginación: 129-148
País: México
Idioma: Inglés
Tipo de documento: Artículo
Resumen en inglés 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.
Disciplinas: Ciencias de la computación
Palabras clave: Inteligencia artificial
Keyword: Sentiment Analysis,
Tunisian Dialect,
Social networks,
Aspect-based Sentiment Analysis,
Sentence-Based sentiment analysis,
Big data,
CNN,
RNN,
Artificial intelligence
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