Advancing natural language processing for Persian movie review analysis: roadmap and opportunities

Abstract

As Persian-language movie platforms gain popularity, analyzing user-generated content becomes increasingly important. Advanced Natural Language Processing (NLP) tools, such as TextBlob, NLTK, VADER, Num2faword, PersianTools, Parsivar, Hazm, and BERT, provide robust methods for sentiment analysis, text preprocessing, and aspect-based analysis tailored to Persian movie reviews. These tools address unique challenges, including diverse writing styles, non-standardized sentiment lexicons, and linguistic complexities of Persian. This paper presents a roadmap for developing NLP-based solutions, highlighting these tools’ applications with example outputs. The integration of these tools into sentiment analysis pipelines offers significant opportunities for improved user experiences, personalized recommendations, and actionable insights for platform owners. By addressing challenges and capitalizing on the potential of advanced NLP techniques, this research aims to foster the growth of Persian-language movie platforms and contribute to their global competitiveness.

Keywords

• sentiment analysis
• natural language processing
• persian language processing
• movie review analysis
• recommender systems