  Social media hold valuable, vast and unstructured information on public
opinion that can be utilized to improve products and services. The automatic
analysis of such data, however, requires a deep understanding of natural
language. Current sentiment analysis approaches are mainly based on word
co-occurrence frequencies, which are inadequate in most practical cases. In
this work, we propose a novel hybrid framework for concept-level sentiment
analysis in Persian language, that integrates linguistic rules and deep
learning to optimize polarity detection. When a pattern is triggered, the
framework allows sentiments to flow from words to concepts based on symbolic
dependency relations. When no pattern is triggered, the framework switches to
its subsymbolic counterpart and leverages deep neural networks (DNN) to perform
the classification. The proposed framework outperforms state-of-the-art
approaches (including support vector machine, and logistic regression) and DNN
classifiers (long short-term memory, and Convolutional Neural Networks) with a
margin of 10-15% and 3-4% respectively, using benchmark Persian product and
hotel reviews corpora.
