What’s This Movie About? A Joint Neural Network Architecture for
            Movie Content Analysis

Gorinski, Philip John; Lapata, Mirella

doi:10.18653/v1/n18-1160

Cited by 20 publications

(19 citation statements)

References 28 publications

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“…AFINN-111 (Nielsen 2011): For a particular sentence, it produces a single score (-5 to +5) by summing the valence (a dimensional measure of positive or negative sentiment) ratings for all words in the sentence. AFINN-111 has also been effectively used for movie summarization (Gorinski and Lapata 2018). VADER (Gilbert 2014): valence aware dictionary and sentiment reasoner (VADER) is a lexicon and rule-based sentiment analyzer that produces a score (-1 to +1) for a document.…”

Section: Methodsmentioning

confidence: 99%

Violence Rating Prediction from Movie Scripts

Martínez

Somandepalli

Singla

et al. 2019

AAAI

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Violent content in movies can influence viewers’ perception of the society. For example, frequent depictions of certain demographics as perpetrators or victims of abuse can shape stereotyped attitudes. In this work, we propose to characterize aspects of violent content in movies solely from the language used in the scripts. This makes our method applicable to a movie in the earlier stages of content creation even before it is produced. This is complementary to previous works which rely on audio or video post production. Our approach is based on a broad range of features designed to capture lexical, semantic, sentiment and abusive language characteristics. We use these features to learn a vector representation for (1) complete movie, and (2) for an act in the movie. The former representation is used to train a movie-level classification model, and the latter, to train deep-learning sequence classifiers that make use of context. We tested our models on a dataset of 732 Hollywood scripts annotated by experts for violent content. Our performance evaluation suggests that linguistic features are a good indicator for violent content. Furthermore, our ablation studies show that semantic and sentiment features are the most important predictors of violence in this data. To date, we are the first to show the language used in movie scripts is a strong indicator of violent content. This offers novel computational tools to assist in creating awareness of storytelling.

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Section: Methodsmentioning

confidence: 99%

Violence Rating Prediction from Movie Scripts

Martínez

Somandepalli

Singla

et al. 2019

AAAI

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“…Other layers from different media are left so far to explore, such as the actual sound component, the DVD chapter decomposition, and even language comparison if we consider different languages of the subtitle tracks. Fusing all sources of information like the proposed model does should come handy in supporting machine learning tasks, such as face recognizers, and movie classification (Gorinski and Lapata 2018;Viard and Fournier-S'niehotta 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Character networks then became a natural focus for story analysis which from literature (Knuth 1993;Waumans et al 2015;Chen et al 2019) expanded to multimedia content (Weng et al 2009;Tan et al 2014;Tran and Jung 2015;Mish 2016;He et al 2018). Particular attention has been paid to dialogue structure (Park et al 2012;Gorinski and Lapata 2018), which leads to an extension of network modeling to multilayer models (Lv et al 2018;Ren et al 2018;Mourchid et al 2018).…”

Section: Related Workmentioning

confidence: 99%

“…They construct a weighted network of characters from dialogue exchanges in order to rank their role importance. Based on a corpus of movie scripts, Gorinski et al (2018) proposed an end-to-end machine learning model for movie overview generation, that uses graph-based features extracted from character-dialogue networks built from movie scripts.…”

Section: Related Workmentioning

confidence: 99%

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Movienet: a movie multilayer network model using visual and textual semantic cues

et al. 2019

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Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc.. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.

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“…Final event of the main story, moment of resolution and the "biggest spoiler". ries (Frermann et al, 2018), summarizing screenplays (Gorinski and Lapata, 2018), and answering questions about long and complex narratives (Kočiskỳ et al, 2018).…”

Section: Climaxmentioning

confidence: 99%

Movie Plot Analysis via Turning Point Identification

Papalampidi¹,

Keller²,

Lapata³

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and thematic units (e.g., setup, complications, aftermath). We propose the task of turning point identification in movies as a means of analyzing their narrative structure. We argue that turning points and the segmentation they provide can facilitate processing long, complex narratives, such as screenplays, for summarization and question answering. We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays. Our model outperforms strong baselines based on state-of-the-art sentence representations and the expected position of turning points.Recently divorced Meg Altman and her 11-year-old daughter Sarah have just purchased a four-story brownstone on New York City. The house's previous owner installed an isolated room used to protect the house's occupants from intruders. On the night the two move into the home, it is broken by Junior, the previous owner's grandson; Burnham, an employee of the residence's security company; and Raoul; a ski mask-wearing gunman.

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What’s This Movie About? A Joint Neural Network Architecture for Movie Content Analysis

Cited by 20 publications

References 28 publications

Violence Rating Prediction from Movie Scripts

Violence Rating Prediction from Movie Scripts

Movienet: a movie multilayer network model using visual and textual semantic cues

Movie Plot Analysis via Turning Point Identification

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