  Sentiment Analysis (SA) is a major field of study in natural language
processing, computational linguistics and information retrieval. Interest in SA
has been constantly growing in both academia and industry over the recent
years. Moreover, there is an increasing need for generating appropriate
resources and datasets in particular for low resource languages including
Persian. These datasets play an important role in designing and developing
appropriate opinion mining platforms using supervised, semi-supervised or
unsupervised methods. In this paper, we outline the entire process of
developing a manually annotated sentiment corpus, SentiPers, which covers
formal and informal written contemporary Persian. To the best of our knowledge,
SentiPers is a unique sentiment corpus with such a rich annotation in three
different levels including document-level, sentence-level, and
entity/aspect-level for Persian. The corpus contains more than 26000 sentences
of users opinions from digital product domain and benefits from special
characteristics such as quantifying the positiveness or negativity of an
opinion through assigning a number within a specific range to any given
sentence. Furthermore, we present statistics on various components of our
corpus as well as studying the inter-annotator agreement among the annotators.
Finally, some of the challenges that we faced during the annotation process
will be discussed as well.
