1. A method for a computer-implemented system and method for decolonial and feminist analysis of gendered trauma in south Asian partition cinema, wherein the method comprising: a data ingestion module designed to process and preprocess multimedia film data such as video, audio, subtitles, scripts, and metadata; a multimodal feature extraction engine designed to extract visual, textual, and acoustic features from multimedia film data; a natural language processing module designed to detect gendered discourse, colonial narratives, trauma indicators, and resistance statements in dialogue and subtitle text; a visual analytics module designed to detect character placement, body language, spatial marginalization, symbolic imagery, and violence-related visual features; a trauma pattern recognition engine employing machine learning models trained on annotated feminist and decolonial theoretical texts to classify and score representations of gendered trauma; a decolonial inference module designed to detect colonial power structures, patriarchal hierarchies, and subaltern silencing patterns; a feminist interpretative layer designed to map extracted features to feminist theoretical concepts such as intersectionality, embodiment, agency, and resistance; a knowledge graph generation module designed to establish relational correspondences between characters, events, spaces, and trauma narratives; and a visualization interface designed to display analytical results such as trauma intensity heatmaps, discourse clustering, character agency scores, and decolonial critique indicators.

Description:[0002] The technical field relates to an interdisciplinary, computer-based system for the automated, theory-driven analysis of gendered representations of trauma in South Asian Partition cinema. The technical field is at the nexus of multimodal machine learning, natural language processing, computer vision, and digital humanities, with a focus on film studies, trauma studies, and decolonial feminist analysis. The invention is particularly relevant to computational modeling of cinematic narratives that represent violence, displacement, and memory related to the Partition through gendered bodies and subjectivities.
[0003] The system falls under the larger technical domain of AI-assisted film analytics, and it employs pipelines for ingesting, annotating, and mining large-scale audiovisual corpora. The technical domain includes the following core technologies: multimodal feature extraction from video, audio, and subtitles; NLP for script and dialogue analysis; computer vision for shot composition and gaze analysis; and graph modeling of character relationships, power hierarchies, and trauma-memory structures. The technical domain also includes the design of trauma-specific ontologies and decolonial-feminist knowledge graphs that encode motifs such as abduction, sexual violence, forced migration, silencing, and post-memory in various South Asian languages and film traditions.
[0004] Moreover, the invention falls under the domain of fairness-aware and bias-sensitive AI, as it tackles the issue of gender bias and colonial epistemologies embedded in automated media analysis systems. The invention also involves bias detection and mitigation techniques in models trained on historical and contemporary film datasets, ensuring that the analytics output emphasizes marginalized voices over dominant nationalist and patriarchal discourses. The invention, therefore, positions itself in the realm of explainable AI, allowing film studies, gender studies, and history scholars to refine computational models and interpretability interfaces. In general, the technical domain includes AI-driven multimodal discourse analysis, digital trauma archives, and specialized interactive visualization tools for the aesthetics and politics of Partition cinema from decolonial and feminist perspectives.
SUMMARY
[0005] In view of the foregoing, an embodiment herein provides a method for a computer-implemented system and method for decolonial and feminist analysis of gendered trauma in south Asian partition cinema. In some embodiments, wherein a computer-based system and method examine gendered trauma in South Asian Partition cinema using decolonial and feminist approaches. The invention processes films about the 1947 Partition of India and Pakistan, specifically examining the representation of violence, displacement, and memory in women's narratives in Punjabi, Bengali, and Urdu cinema.
[0006] The system takes in multimodal inputs of video, audio, subtitles, and metadata through automated processing. Major components include: a trauma ontology database indexing Partition-related motifs of abduction, honor killings, sexual violence, and intergenerational silencing; natural language processing models for dialogue sentiment analysis and narrative structure detection; computer vision models for the identification of visual trauma signs of gaze avoidance, bodily disintegration, and spatial disorientation; and graph neural networks for modeling intersectional relationships of gender, caste, religion, and colonialism.
[0007] Trained on annotated datasets from iconic films, the tool is highly precise in motif detection and balances gender and cultural biases using fairness-aware fine-tuning. An interactive dashboard provides visualizations, simulations, and explanations, empowering researchers to resist Eurocentric historiographies and give voice to subaltern narratives. This scalable solution connects digital humanities and AI, facilitating ethical re-interpretations of the gendered Partition legacy.
[0008] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0010] FIG. 1 illustrates a method for a computer-implemented system and method for decolonial and feminist analysis of gendered trauma in south Asian partition cinema according to an embodiment herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0011] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0012] FIG. 1 illustrates a method for a computer-implemented system and method for decolonial and feminist analysis of gendered trauma in south Asian partition cinema according to an embodiment herein. In some embodiments, a computer-implemented system that enables decolonial and feminist analysis of gendered trauma in South Asian Partition cinema through a modular, end-to-end multimodal AI system architecture. In a representative embodiment, the system includes a data ingestion module, a trauma ontology and knowledge graph module, a multimodal analytics engine module, a bias and decoloniality module, and an interactive research interface module, all running on one or more networked servers or cloud instances that are accessible from client devices. The data ingestion module retrieves full-length films, trailers, scripts, subtitle files, paratexts (censorship reports, posters, and synopses), and expert annotations for works of South Asian cinema that depict the 1947 Partition and its aftermath in Indian, Pakistani, and Bangladeshi cinemas. A preprocessing module normalizes frame rates, compresses audio, synchronizes subtitles with dialogue, and breaks down films into scenes, shots, and narrative units using shot boundary detection and temporal clustering. In the preferred embodiments, language normalization modules process Hindi-Urdu, Punjabi, Bengali, English, and code-switched scripts, involving tokenization, transliteration, and machine translation while maintaining culturally specific vocabularies for gender, honor, kinship, and violence.
[0013] The trauma-ontology and knowledge graph layer formalizes Partition-specific motifs—abduction, forced conversion, communal riots, border crossing, refugee camps, state surveillance, honor killings, reproductive violence, and silencing of survivors—that are organized as domain entities with hierarchical and relational properties. Ontology modeling is informed by trauma studies, Partition historiography, feminist film theory, and decolonial thought to articulate classes and their relationships to film forms such as miseenscène, sound, and narrative focalization. These ontology concepts are realized in a knowledge graph where nodes correspond to characters, institutions, locations, objects, and events, and edges represent relationships such as kinship, coercion, protection, betrayal, and displacement. In preferred embodiments, the knowledge graph is dynamically updated as analytical modules deduce new edges, such as the patterns of how women’s bodies stand in for territorial sovereignty or communal honor in particular films or subgenres.
[0014] The multimodal analytics engine combines the three key submodules: natural language processing (NLP), computer vision, and audio-paralinguistic analysis, which are integrated using attention-based or graph-based models. The NLP submodule analyzes scripts, subtitles, and paratexts to conduct named entity recognition, coreference resolution, sentiment and emotion classification, topic modeling, and narrative role labeling with a special emphasis on gendered subject positions and trauma discourses. It identifies discursive patterns such as sacrificial motherhood, redemptive nationalism, or victim-perpetrator reversals, which are then linked to the trauma ontology on the basis of semantic similarity and rule-based mappings. The computer vision submodule derives shot-level visual features such as camera distance, camera angle, body framing, occlusions, use of thresholds and borders (doors, trains, fences, rivers), and signs of spatial confinement or exposure, which are often pivotal to cinematic depictions of Partition trauma. Violence- and trauma-related features such as rapid motion, crowding, color desaturation, fire, blood, and fragmented or hidden bodies are detected using CNNs or transformers that are trained or fine-tuned on carefully annotated film corpora, and then reasoned about using ontology-guided reasoning to separate sensationalized spectacle from affective, survivor-cantered aesthetics. The audio module analyzes prosody, pitch, volume, silence, non-verbal vocalizations (screams, sobs), crowd noise, and soundscapes (trains, marches, gunfire, slogans) that are imbued with affective and political meaning, and combines these with text content to compute composite trauma features.
[0015] In the preferred embodiments, a fusion layer is used to combine the outputs of the three submodules via multimodal attention networks, late fusion with meta-classifiers, or graph neural networks that directly work on the cinematic knowledge graph. This allows for the inference of the distribution and intensity of gendered trauma motifs, their evolution over time, and their connection to particular character groups (such as women of a certain religious or caste affiliation) at the scene and movie levels. The system has training and inference phases. During training, the system uses expert-annotated scenes and characters from canonical Partition movies to learn the mappings between low-level features and high-level ontological categories. During inference, the system automatically analyzes unlabeled movies, and the proposed labels and links are provided to human experts for validation or correction.
[0016] A bias and decoloniality module, in a preferred embodiment, is responsible for monitoring and correcting the algorithmic bias that could result in the perpetuation of hegemonic discourses such as the predominance of male protagonists, the primacy of dominant languages, or the reading of non-Western aesthetics in terms of Eurocentric violence taxonomies. The bias and decoloniality module would utilize counterfactual data augmentation (such as reweighting the scenes where the testimonies of women are prioritized), adversarial debiasing of embeddings, and fairness metrics that are stratified by gender, language, religion, and region. It would also implement feminist and decolonial thought by prioritizing the subaltern voices and structurally marginalized characters, as well as non-state narratives, in accordance with theoretical imperatives to decolonize the coloniality of power and epistemic extraction in AI.
[0017] The interactive research environment is implemented as a web-based dashboard or desktop application that enables researchers to investigate films, scenes, and characters using trauma-based and feminist-decolonial perspectives. Researchers can search the system for particular themes and obtain results such as timeline graphs of trauma levels, gender relationship networks, and heat maps of camera attention to females' bodies versus male authority figures. Drill-down capabilities allow researchers to examine model explanations to facilitate validation and argumentation against machine interpretations. In certain versions, generative elements can model hypothetical edits or story perspectives to facilitate decolonial, speculative re-imaginings while distinguishing these results as hypothetical, non-archival artifacts. , Claims:I/We Claim:
1. A method for a computer-implemented system and method for decolonial and feminist analysis of gendered trauma in south Asian partition cinema, wherein the method comprising:
a data ingestion module designed to process and preprocess multimedia film data such as video, audio, subtitles, scripts, and metadata;
a multimodal feature extraction engine designed to extract visual, textual, and acoustic features from multimedia film data;
a natural language processing module designed to detect gendered discourse, colonial narratives, trauma indicators, and resistance statements in dialogue and subtitle text;
a visual analytics module designed to detect character placement, body language, spatial marginalization, symbolic imagery, and violence-related visual features;
a trauma pattern recognition engine employing machine learning models trained on annotated feminist and decolonial theoretical texts to classify and score representations of gendered trauma;
a decolonial inference module designed to detect colonial power structures, patriarchal hierarchies, and subaltern silencing patterns;
a feminist interpretative layer designed to map extracted features to feminist theoretical concepts such as intersectionality, embodiment, agency, and resistance;
a knowledge graph generation module designed to establish relational correspondences between characters, events, spaces, and trauma narratives; and
a visualization interface designed to display analytical results such as trauma intensity heatmaps, discourse clustering, character agency scores, and decolonial critique indicators.