This study develops a thematic model for characterizing and comparing individual films and film corpora across genre, language, and time through narrative orientations while preserving the interpretive flexibility central to film studies. Although cinema scholarship has produced important studies of themes, genres, auteurs, and ideologies, fewer efforts have developed transferable frameworks for systematic comparative thematic analysis. This study addresses that gap through the Seven Lenses Framework for Cinema (SLFC). SLFC was developed through an iterative qualitative framework-construction process involving open, affinity, axial, and selective coding. The analysis began with approximately 100 cinema-related concepts drawn from film theory and social science and was refined through the examination of critical reviews, plot summaries, trailers, audience discussions, and a framework-development corpus of 154 films. This process generated approximately 170 recurring concepts that were consolidated into first-order codes, thematic clusters, and seven lenses: Institutional Justice, Emotional Realism, Trauma and Survival, Marginalized Identities, Psychological Interiority, Youth Identity and Aspiration, and Genre Hybridity and Experimentation. The framework was subsequently applied to a corpus of 970 Indian films from the Malayalam and Tamil film industries released between 2021 and 2025. Chi-square and year-wise analyses identify Emotional Realism, Institutional Justice, and Genre Hybridity as the dominant thematic orientations. The findings suggest that the two industries are organized around a shared thematic structure, with variation primarily in the relative prominence of individual themes. The study proposes SLFC as a transferable framework for comparative thematic analysis across diverse film traditions and other narrative media.

Authors:
Sreejith Srikrishnan, Tiger Analytics, India
Srikrishnan Sundararajan, Adi Shankara Institute of Engineering and Technology, India

About the Presenter(s)
Sreejith Srikrishnan is a Data Scientist at Tiger Analytics with interests in artificial intelligence, machine learning, and network analysis. His current work explores data-driven intelligent systems and AI for decision-making.

Connect on Linkedin
https://in.linkedin.com/in/sreejithsrikrishnan

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