  Aspect-based sentiment analysis is of great importance and application
because of its ability to identify all aspects discussed in the text. However,
aspect-based sentiment analysis will be most effective when, in addition to
identifying all the aspects discussed in the text, it can also identify their
polarity. Most previous methods use the pipeline approach, that is, they first
identify the aspects and then identify the polarities. Such methods are
unsuitable for practical applications since they can lead to model errors.
Therefore, in this study, we propose a multi-task learning model based on
Convolutional Neural Networks (CNNs), which can simultaneously detect aspect
category and detect aspect category polarity. creating a model alone may not
provide the best predictions and lead to errors such as bias and high variance.
To reduce these errors and improve the efficiency of model predictions,
combining several models known as ensemble learning may provide better results.
Therefore, the main purpose of this article is to create a model based on an
ensemble of multi-task deep convolutional neural networks to enhance sentiment
analysis in Persian reviews. We evaluated the proposed method using a Persian
language dataset in the movie domain. Jacquard index and Hamming loss measures
were used to evaluate the performance of the developed models. The results
indicate that this new approach increases the efficiency of the sentiment
analysis model in the Persian language.
