Researchers from the College of Information Technology Publish a Scientific Study on MPAA Movie Rating Classification Using Textual and Emotional Analysis
Duhaa Fadill Abbas
In line with the Department of Software’s orientation at the College of Information Technology toward promoting high quality scientific research and strengthening the international academic presence of its researchers PhD candidate Yaseen Khudhair Abbas from the Department of Software in collaboration with Dr Ahmed Habib Al Azzawi has published a specialized scientific study addressing advanced techniques for classifying movies according to Motion Picture Association of America MPAA ratings based on textual and emotional feature analysis.
The study entitled Emotions and Textual Feature Analysis for Motion Picture Association of America MPAA Rating Classification has been published in the Proceedings of the International Conference on Applied Innovation in IT which is indexed in Scopus reflecting the academic significance and scientific value of this research contribution.
The study proposes an advanced automated approach for predicting MPAA ratings through analyzing movie scripts and extracting significant linguistic patterns using TF IDF techniques alongside emotional features derived from transformer based models known for their contextual understanding capabilities.
Furthermore the research integrates textual and emotional features within a multi class classification framework utilizing the LightGBM algorithm which enhances prediction accuracy and provides a more reliable assessment of content suitability for different age groups compared to traditional manual rating approaches.
This research aims to support the development of intelligent systems capable of more efficient and objective movie content classification thereby improving user experience enabling families to make informed viewing decisions and advancing research applications in the fields of text analysis and multimedia processing.
Research Link :
https://www.icaiit.org/paper.php?paper=13th_ICAIIT_5/3_1