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Over recent years a lot of research papers and studies have been published onthe development of effective approaches that benefit from a large amount ofuser-generated content and build intelligent predictive models on top of them.This research applies machine learning-based approaches to tackle the hurdlesthat come with Persian user-generated textual content. Unfortunately, there isstill inadequate research in exploiting machine learning approaches toclassify/cluster Persian text. Further, analyzing Persian text suffers from alack of resources; specifically from datasets and text manipulation tools.Since the syntax and semantics of the Persian language is different fromEnglish and other languages, the available resources from these languages arenot instantly usable for Persian. In addition, recognition of nouns andpronouns, parts of speech tagging, finding words' boundary, stemming orcharacter manipulations for Persian language are still unsolved issues thatrequire further studying. Therefore, efforts have been made in this research toaddress some of the challenges. This presented approach uses amachine-translated datasets to conduct sentiment analysis for the Persianlanguage. Finally, the dataset has been rehearsed with different classifiersand feature engineering approaches. The results of the experiments have shownpromising state-of-the-art performance in contrast to the previous efforts; thebest classifier was Support Vector Machines which achieved a precision of91.22%, recall of 91.71%, and F1 score of 91.46%.

The Challenges of Persian User-generated Textual Content: A Machine Learning-Based Approach