  Over recent years a lot of research papers and studies have been published on
the development of effective approaches that benefit from a large amount of
user-generated content and build intelligent predictive models on top of them.
This research applies machine learning-based approaches to tackle the hurdles
that come with Persian user-generated textual content. Unfortunately, there is
still inadequate research in exploiting machine learning approaches to
classify/cluster Persian text. Further, analyzing Persian text suffers from a
lack of resources; specifically from datasets and text manipulation tools.
Since the syntax and semantics of the Persian language is different from
English and other languages, the available resources from these languages are
not instantly usable for Persian. In addition, recognition of nouns and
pronouns, parts of speech tagging, finding words' boundary, stemming or
character manipulations for Persian language are still unsolved issues that
require further studying. Therefore, efforts have been made in this research to
address some of the challenges. This presented approach uses a
machine-translated datasets to conduct sentiment analysis for the Persian
language. Finally, the dataset has been rehearsed with different classifiers
and feature engineering approaches. The results of the experiments have shown
promising state-of-the-art performance in contrast to the previous efforts; the
best classifier was Support Vector Machines which achieved a precision of
91.22%, recall of 91.71%, and F1 score of 91.46%.
