  Sentiment analysis attempts to identify, extract and quantify affective
states and subjective information from various types of data such as text,
audio, and video. Many approaches have been proposed to extract the sentiment
of individuals from documents written in natural languages in recent years. The
majority of these approaches have focused on English, while resource-lean
languages such as Persian suffer from the lack of research work and language
resources. Due to this gap in Persian, the current work is accomplished to
introduce new methods for sentiment analysis which have been applied on
Persian. The proposed approach in this paper is two-fold: The first one is
based on classifier combination, and the second one is based on deep neural
networks which benefits from word embedding vectors. Both approaches takes
advantage of local discourse information and external knowledge bases, and also
cover several language issues such as negation and intensification,
andaddresses different granularity levels, namely word, aspect, sentence,
phrase and document-levels. To evaluate the performance of the proposed
approach, a Persian dataset is collected from Persian hotel reviews referred as
hotel reviews. The proposed approach has been compared to counterpart methods
based on the benchmark dataset. The experimental results approve the
effectiveness of the proposed approach when compared to related works.
