Movie Genre Research Articles

Exploring Genre-Specific Thought Patterns Through Real-Time Experience Sampling of Movie Clips

Understanding cognition during media consumption is crucial to understanding how individuals process complex stimuli in everyday life. This study employed multi-dimensional Experience Sampling (mDES) to capture real-time thoughts as participants viewed 2-3 minute movie clips across various genres. By utilizing movie clips, this study bridges real-world scenarios with the control of a laboratory setting; revealing how different movie genres uniquely shape thought patterns. I conducted Principal Component Analysis to identify the underlying structure of participants' thoughts. The analysis revealed four distinct thought components, named based on their loading structure: (1) Narrative Comprehension, (2) Episodic Knowledge, (3) Intrusive Distraction, and (4) Sensory Engagement, replicating components found in earlier mDES movie-watching studies (Konu et al., 2021; Wallace et al., 2024). To examine how subjects’ thoughts differed based on the genre of the movie clip, I conducted a series of linear mixed models comparing component scores for each component across genres. The analysis indicated certain thought patterns differed by genre. In Intrusive Distraction, Comedy clips scored significantly lower than Drama clips (p <.001), Romantic Comedy clips (p = .007), and clips of an unspecified genre (other) (p = .010). In Episodic Knowledge, Thriller clips scored significantly lower than Action clips (p = .002), Comedy clips (p <.001), Drama clips (p <.001), Family clips (p <.001), and Romantic Comedy clips (p <.001). Altogether, this study replicates the component structure found in earlier movie watching mDES studies, showing consistency across samples. Additionally, this study makes a significant contribution to the field of ongoing thought research by being the first to empirically establish that individuals’ internal experiences systematically differ based on movie genre. These findings suggest researchers should consider genre-specific influences when designing future movie-based tasks in psychology and neuroimaging studies. However, the small sample size (N=15) warrants caution; larger studies are needed for improved reliability and validity.

Movie trailer genre classification using multimodal pretrained features

We introduce a novel method for movie genre classification, capitalizing on a diverse set of readily accessible pretrained models. These models extract high-level features related to visual scenery, objects, characters, text, speech, music, and audio effects. To intelligently fuse these pretrained features, we train small classifier models with low time and memory requirements. Employing the transformer model, our approach utilizes all video and audio frames of movie trailers without performing any temporal pooling, efficiently exploiting the correspondence between all elements, as opposed to the fixed and low number of frames typically used by traditional methods. Our approach fuses features originating from different tasks and modalities, with different dimensionalities, different temporal lengths, and complex dependencies as opposed to current approaches. Our method outperforms state-of-the-art movie genre classification models in terms of precision, recall, and mean average precision (mAP). To foster future research, we make the pretrained features for the entire MovieNet dataset, along with our genre classification code and the trained models, publicly available.

Automatic movie genre classification & emotion recognition via a BiProjection Multimodal Transformer

Analyzing, manipulating, and comprehending data from multiple sources (e.g., websites, software applications, files, or databases) and of diverse modalities (e.g., video, images, audio and text) has become increasingly important in many domains. Despite recent advances in multimodal classification (MC), there are still several challenges to be addressed, such as: the combination of modalities of very diverse nature, the optimal feature engineering for each modality, as well as the semantic alignment between text and images. Accordingly, the main motivation of our research relies in devising a neural architecture that effectively processes and combines text, image, video and audio modalities, so it can offer a noteworthy performance in different MC tasks. In this regard, the Multimodal Transformer (MulT) model is a cutting-edge approach often employed in multimodal supervised tasks, which, although effective, has the problem of having a fixed architecture that limits its performance in specific tasks as well as its contextual understanding, meaning it may struggle to capture fine-grained temporal patterns in audio or effectively model spatial relationships in images. To address these issues, our research modifies and extends the MulT model in several aspects. Firstly, we focus on leveraging the Gated Multimodal Unit (GMU) module within the architecture to efficiently and dynamically weigh modalities at the instance level and to visualize the use of modalities. Secondly, to overcome the problem of vanishing and exploding gradients we focus on strategically placing residual connections in the architecture. The proposed architecture is evaluated in two different and complex classification tasks, on the one hand, the movie genre categorization (MGC) and, on the other hand, the multimodal emotion recognition (MER). The results obtained are encouraging as they indicate that the proposed architecture is competitive against state-of-the-art (SOTA) models in MGC, outperforming them by up to 2% on the Moviescope dataset, and by 1% on the MM-IMDB datasets. Furthermore, in the MER task the unaligned version of the datasets was employed, which is considerably more difficult; we improve accuracy SOTA results by up to 1% on the IEMOCAP dataset, and attained a competitive outcome on the CMU-MOSEI11Dai et al., (2021) collection, outperforming SOTA results in several emotions.

Stag tourism and scripted liminality

Stag party tourism is a highly ritualized contemporary rite of passage that is associated with drunken excess and norm-breaking toxic masculinity. Despite contravening societal rules and encouraging deviant behaviour, stag parties are generally tolerated. This paper explores how stag tourism is a constructed rite of passage and a scripted liminality, packaged by destination marketers, staged by service providers, and performed enthusiastically by participants. Analysis of interview and observational data suggests that the bachelor movie genre is the inspiration which informs expectations and experience. Indeed, the degree of inter-textuality between on-screen (reel life) and off-screen (real life) performances confirms that this is a scripted liminality, informed not by elders or peers, but by Hollywood film makers. We conclude with a discussion of a series of contributions and implications for practice.

Artificial intelligence and environment behavior psychology based evolution of science fiction movie genres

Artificial intelligence and environment behavior psychology based evolution of science fiction movie genres

REPRESENTASI NILAI MORAL PADA FILM BUDI PEKERTI KARYA WREGAS BHANUTEJA (KAJIAN SOSIOLOGI SASTRA)

Movies not only entertain the audience, but also function as a means of conveying the messages they contain. Therefore, there are currently a lot of conversations about domestically and internationally produced films. The development of this movie has become a hot topic of conversation in the community. One of the movies that is currently hitting the audience is the movie 'Budi Pekerti' by Wregas Bhanuteja. The film tells the story of a guidance and counseling teacher who uses his unique technique to guide his students. Until one day, the teacher suddenly becomes a hot topic on social media. This affects his career and family. This drama genre movie presents realistic nuanced stories that are currently popular. The purpose of this research is to find out the moral values contained in the movie "Budi Pekerti". This research uses a qualitative descriptive method. The results of the research on the Budi Pekerti movie obtained by researchers are knowing the forms of cyberbullying and five moral values in the movie "Budi Pekerti". The five moral values are: honesty, hard work, creativity, critical thinking, and environmental care. Based on these five moral values, it can be concluded that the Budi Pekerti film is an educational spectacle and can instill good moral values for teenagers in the future.

Marvel Movie Recommendation System Using Hybrid Item-Based and Content-Based Filtering Methods

Currently, there are so many movie genres available to the general public, making it difficult for viewers to choose a movie. One of the most popular movies is the “Marvel Movies” or MCU (Marvel Cinematic Universe), which has become the highest grossing franchise of all time with 90 movies released. The large number of movie titles makes it difficult for people to choose which movie to watch. Therefore, a Marvel movie recommendation system is needed using a hybrid item-based and content-based filtering method. The content-based method calculates the similarity between movies by identifying similar Marvel movies based on content such as genre, actor, director, and synopsis. Meanwhile, item-based completes content-based recommendations by considering user preferences. The reason for using the hybrid item-based and content-based filtering method is to be able to produce more accurate recommendations than a single method. The types and sources of data used are secondary data from journals and the internet (Imdb and Movielens), as well as datasets about Marvel movies. From the results of testing the hybrid model, the precision value is 0.8 or 80% which indicates that the model is accurate. In item-based filtering testing, the similarity result of 0.68 shows good item similarity. In the content-based filtering test, the highest similarity is 0.14 and the lowest similarity is 0.10 which shows that the similarity between the searched content and the generated content is relevant.

Exploration of Speech and Music Information for Movie Genre Classification

Movie genre prediction from trailers is mostly attempted in a multi-modal manner. However, the characteristics of movie trailer audio indicate that this modality alone might be highly effective in genre prediction. Movie trailer audio predominantly consists of speech and music signals in isolation or overlapping conditions. This work hypothesizes that the genre labels of movie trailers might relate to the composition of their audio component. In this regard, speech-music confidence sequences for the trailer audio are used as a feature. In addition, two other features previously proposed for discriminating speech-music are also adopted in the current task. This work proposes a time and channel Attention Convolutional Neural Network (ACNN) classifier for the genre classification task. The convolutional layers in ACNN learn the spatial relationships in the input features. The time and channel attention layers learn to focus on crucial timesteps and CNN kernel outputs, respectively. The Moviescope dataset is used to perform the experiments, and two audio-based baseline methods are employed to benchmark this work. The proposed feature set with the ACNN classifier improves the genre classification performance over the baselines. Moreover, decent generalization performance is obtained for genre prediction of movies with different cultural influences (EmoGDB).

How movies move us - movie preferences are linked to differences in neuronal emotion processing of fear and anger: an fMRI study.

As a source of audio-visual stimulation, movies expose people to various emotions. Interestingly, several genres are characterized by negative emotional content. Albeit theoretical approaches exist, little is known about preferences for specific movie genres and the neuronal processing of negative emotions. We investigated associations between movie genre preference and limbic and reward-related brain reactivity to close this gap by employing an fMRI paradigm with negative emotional faces in 257 healthy participants. We compared the functional activity of the amygdala and the nucleus accumbens (NAcc) between individuals with a preference for a particular movie genre and those without such preference. Amygdala activation was relatively higher in individuals with action movie preference (p TFCE-FWE = 0.013). Comedy genre preference was associated with increased amygdala (p TFCE-FWE = 0.038) and NAcc activity (p TFCE-FWE = 0.011). In contrast, crime/thriller preference (amygdala: p TFCE-FWE ≤ 0.010, NAcc: p TFCE-FWE = 0.036), as well as documentary preference, was linked to the decreased amygdala (p TFCE-FWE = 0.012) and NAcc activity (p TFCE-FWE = 0.015). The study revealed associations between participants' genre preferences and brain reactivity to negative affective stimuli. Interestingly, preferences for genres with similar emotion profiles (action, crime/thriller) were associated with oppositely directed neural activity. Potential links between brain reactivity and susceptibility to different movie-related gratifications are discussed.

StreamBoostE: A Hybrid Boosting-Collaborative Filter Scheme for Adaptive User-Item Recommender for Streaming Services

With home entertainment, selecting the perfect movie is a pervasive challenge, amplified by many streaming platforms like Netflix and Amazon Prime. This study advances a movie recommender system with collaborative filtering approach as implemented in Python titled StreamBoostE. We used the user-based and item-based similarity schemes on feature embedding to aid faster model construction and training for the tree-based gradient boosting ensemble. Employing both user- and item-based collaborative filtering with cosine similarity to ease feature embedding, the system assesses movies inter-relations via personalized user interest and preferences as submitted user titles with a focus on movie genre classification. Results shows the ensemble yields a recommender prediction accuracy of 0.9984 with F1 of 0.996. The major contribution of StreamBoostE is in its capability to expedite the movie selection process when integrated using flask API and streamlit for cross-channel integration in web-based platforms. It presents users with a list of top-10 recommended movies by genre similarity. The XGBoost ensemble performed best with the user-/item-based collaborative filtering scheme fused with feature embedding approach as a sampling method. Keywords: Random Forest, SMOTE, credit card fraud detection, feature selection, imbalanced dataset Aims Research Journal Reference Format: Atuduhor, R.R., Okpor, M.D., Yoro, R.E., Odiakaose, C.C., Emordi, F.U., Ojugo, A.A., Ako, R.E., Geteloma, V.O., Ejeh, P.O., Abere, R.A., Ifioko, A.M., & Brizimor, S.E. (2024): StreamBoostE: A Hybrid Boosting-Collaborative Filter Scheme for Adaptive User-Item Recommender for Streaming Services. Advances in Multidisciplinary and Scientific Research Journal Vol. 10. No. 2. Pp 89-106. www.isteams.net/aimsjournal. dx.doi.org/10.22624/AIMS/V10N2P8

Enhancing multimedia management: cloud-based movie type recognition with hybrid deep learning architecture

Film and movie genres play a pivotal role in captivating relevant audiences across interactive multimedia platforms. With a focus on entertainment, streaming providers are increasingly prioritizing the automatic generation of movie genres within cloud-based media services. In service management, the integration of a hybrid convolutional network proves to be instrumental in effectively distinguishing between a diverse array of video genres. This classification process not only facilitates more refined recommendations and content filtering but also enables targeted advertising. Furthermore, given the frequent amalgamation of components from various genres in cinema, there arises a need for social media networks to incorporate real-time video classification mechanisms for accurate genre identification. In this study, we propose a novel architecture leveraging deep learning techniques for the detection and classification of genres in video films. Our approach entails the utilization of a bidirectional long- and short-term memory (BiLSTM) network, augmented with video descriptors extracted from EfficientNet-B7, an ImageNet pre-trained convolutional neural network (CNN) model. By employing BiLSTM, the network acquires robust video representations and proficiently categorizes movies into multiple genres. Evaluation on the LMTD dataset demonstrates the substantial improvement in the performance of the movie genre classifier system achieved by our proposed architecture. Notably, our approach achieves both computational efficiency and precision, outperforming even the most sophisticated models. Experimental results reveal that EfficientNet-BiLSTM achieves a precision rate of 93.5%. Furthermore, our proposed architecture attains state-of-the-art performance, as evidenced by its F1 score of 0.9012.

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.

Epistemic language in news headlines shapes readers’ perceptions of objectivity

How we reason about objectivity-whether an assertion has a ground truth-has implications for belief formation on wide-ranging topics. For example, if someone perceives climate change to be a matter of subjective opinion similar to the best movie genre, they may consider empirical claims about climate change as mere opinion and irrelevant to their beliefs. Here, we investigate whether the language employed by journalists might influence the perceived objectivity of news claims. Specifically, we ask whether factive verb framing (e.g., "Scientists know climate change is happening") increases perceived objectivity compared to nonfactive framing (e.g., "Scientists believe [...]"). Across eight studies (N = 2,785), participants read news headlines about unique, noncontroversial topics (studies 1a-b, 2a-b) or a familiar, controversial topic (climate change; studies 3a-b, 4a-b) and rated the truth and objectivity of the headlines' claims. Across all eight studies, when claims were presented as beliefs (e.g., "Tortoise breeders believe tortoises are becoming more popular pets"), people consistently judged those claims as more subjective than claims presented as knowledge (e.g., "Tortoise breeders know…"), as well as claims presented as unattributed generics (e.g., "Tortoises are becoming more popular pets"). Surprisingly, verb framing had relatively little, inconsistent influence over participants' judgments of the truth of claims. These results demonstrate how, apart from shaping whether we believe a claim is true or false, epistemic language in media can influence whether we believe a claim has an objective answer at all.

Creation of the Gods I: Kingdom of Storms Box Office Affected by Geographical Differences in China

With economic development, more and more people choose to watch movies for leisure and entertainment. With the recent release of Creation of the Gods I: Kingdom of Storms, the writer finds that the box office varies greatly from region to region. The main contributing factors are consumption preferences in different city tiers, the number of roadshows held, and the population size gap. The author will conduct a more in-depth study on people’s preferences for movie genres and consumption preferences in different city tiers. Also, the writer will analyze how the distribution of Creation of the Gods I in different city tiers influences the box office. Explore the causes of these problems and make suggestions: stimulate first-tier city residents to spend more time watching films, hold more roadshows, and refer to more data for comprehensively judging a film. Not only will this help increase the box office for the next part, but it will also increase the overall audience’s motivation to see the movie.

Research on Box Office Influencing Factors and Coping Strategies of Chinese War Genre Movies

Chinese audiences' keen interest in the historical events of wars at home and abroad and their passion for heroism and national emotions have made war genre movies popular in the Chinese market. This paper aims to discuss the box office influencing factors and coping strategies of Chinese war genre movies. With the continuous expansion of China's movie market, war genre movies, as an important movie genre, have attracted wide audience attention. This study adopts a combination of qualitative and quantitative analysis to study the box office performance and influencing factors of war genre movies in the Chinese market through literature review, box office data analysis, and audience survey, and proposes corresponding coping strategies. It is found that various factors, including the choice of theme, director, cast and marketing strategy, influence the box office of Chinese war genre movies. Aiming at these factors, this paper proposes a series of coping strategies to improve the box office performance of war genre movies and, at the same time, promote the healthy development of China's movie industry.

Reading Humor in David Cronenberg's Crash

David Cronenberg’s statement that “The film is funny, the book is not very funny” (1997) presents a captivating paradox regarding his adaptation of J. G. Ballard’s Crash. This statement reveals Cronenberg as both an insightful and superficial reader of Ballard. His film adaptation chooses to remain faithful to many aspects of the source material and retains the subtle undercurrents of humor found in the book, often overlooked and unrecognized by both the director and audiences, as well critics. Paradoxically, the film’s reception has further obscured this aspect of his work. The paper delineates the mechanics of humor in Crash, applying Freudian and Bergsonian theories linking humor to anxiety relief and behavioral correction. It spotlights Ballard’s absurd character speculations and detached narrative voice juxtaposing poetic violence, while Cronenberg conveys humor via affectless performances, anti-climactic sex, and self-referential jokes exposing cinematic artifice. Further analysis explores Ballard’s satire through the parody of melodramatic tableaux versus Cronenberg’s meta-cinematic parody of Hollywood and the road movie genre, noting that the adaptation compresses the novel’s sociopolitical critique but expands its intertextuality. The article challenges assumptions that humor must be light or affirming, arguing that the unsettling, avant-garde laughter in Crash (1973, 1996) compels reflection on technology’s impacts on identity. Ultimately, recognizing the dark comedy in Crash and its adaptation illuminates their lingering cultural commentary on dehumanization rather than celebrating the pathology of the characters.

DC-NAS: Divide-and-Conquer Neural Architecture Search for Multi-Modal Classification

Neural architecture search-based multi-modal classification (NAS-MMC) methods can individually obtain the optimal classifier for different multi-modal data sets in an automatic manner. However, most existing NAS-MMC methods are dramatically time consuming due to the requirement for training and evaluating enormous models. In this paper, we propose an efficient evolutionary-based NAS-MMC method called divide-and-conquer neural architecture search (DC-NAS). Specifically, the evolved population is first divided into k+1 sub-populations, and then k sub-populations of them evolve on k small-scale data sets respectively that are obtained by splitting the entire data set using the k-fold stratified sampling technique; the remaining one evolves on the entire data set. To solve the sub-optimal fusion model problem caused by the training strategy of partial data, two kinds of sub-populations that are trained using partial data and entire data exchange the learned knowledge via two special knowledge bases. With the two techniques mentioned above, DC-NAS achieves the training time reduction and classification performance improvement. Experimental results show that DC-NAS achieves the state-of-the-art results in term of classification performance, training efficiency and the number of model parameters than the compared NAS-MMC methods on three popular multi-modal tasks including multi-label movie genre classification, action recognition with RGB and body joints and dynamic hand gesture recognition.

Movie genre prediction based on the bidirectional encoder representations from transformer

The rapid expansion of digital media has underscored the growing significance of predicting genres to target audiences effectively and to enhance filmmakers' understanding of viewer preferences. In this study, we introduced a novel method for forecasting movie genres, leveraging the power of the Bidirectional Encoder Representations from Transformer (BERT) deep learning model. The research team employed a dataset sourced from Douban's website, which featured 5,000 movies, complete with cover images, titles, and genre information. This undertaking tackled several key challenges, including the extraction of features from textual data, the categorization of movie genres, and the incorporation of cultural nuances. BERT's bidirectional representation, especially its variant tailored for Chinese language tasks, 'bert-base-chinese', was employed to extract textual features. Meanwhile, the visual features from movie covers were processed using the Wide ResNet-50-2 architecture. The combined features underwent classification, and the resulting model achieved an accuracy of 34.67%, a recall rate of 74.5%, and an F1 score of 0.4755. The study validates the potential of the BERT model in predicting movie genres and offers significant insights for future research in multimedia content classification.

Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans

A flood of information has occurred, making it challenging for people to find and filter their favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; however, traditional Appenrecommendation systems, including collaborative filtering, and content-based filtering, face significant challenges such as data scalability, data scarcity, and the cold-start problem, all of which require advanced solutions. Therefore, we propose a ranking and enhancing sequence movie recommendation system that utilizes the combination model of deep learning to resolve the existing issues. To mitigate these challenges, we design an RSs model that utilizes user information (age, gender, occupation) to analyze new users and match them with others who have similar preferences. Initially, we construct sequences of user behavior to effectively predict the potential next target movie of users. We then incorporate user information and movie sequence embeddings as input features to reduce the dimensionality, before feeding them into a transformer architecture and multilayer perceptron (MLP). Our model integrates a transformer layer with positional encoding for user behavior sequences and multi-head attention mechanisms to enhance prediction accuracy. Furthermore, the system applies KMeans clustering to movie genre embeddings, grouping similar movies and integrating this clustering information with predicted ratings to ensure diversity in the personalized recommendations for target users. Evaluating our model on two MovieLens datasets (100 Kand 1 M) demonstrated significant improvements, achieving RMSE, MAE, precision, recall, and F1 scores of 1.0756, 0.8741, 0.5516, 0.3260, and 0.4098 for the 100 K dataset, and 0.9927, 0.8007, 0.5838, 0.4723, and 0.5222 for the 1 M dataset, respectively. This approach not only effectively mitigates cold-start and scalability issues but also surpasses baseline techniques in Top-N item recommendations, highlighting its efficacy in the contemporary environment of abundant data.

Multi-domain aspect-oriented sentiment analysis for movie recommendations using feature extraction

<span>Sentiment analysis is a well-recognized research field that has acknowledged significant attention in recent years. Researchers have made extensive efforts in employing various methodologies to explore these domains. Sentiment classification plays a fundamental role in natural language processing (NLP). However, studies have shown that sentiment classification models heavily depend on the specific domain. In the context of movie industry, where the demand for reliable movie reviews is high and not all movies are of exceptional quality and worthy of viewers time. Therefore, people depend on movie reviews before watching a movie. This explores the use of data from various domains to improve classification performance within each domain, addressing the difficulty of multi-domain sentiment classification in natural language processing. Therefore, it is crucial to effectively utilize shared sentiment knowledge across different domains for real-world applications. To solve these issues, a multi-domain aspect-oriented sentiment analysis for movie recommendation using feature extraction techniques. The main contribution of this work is to eliminate the time for users to go through a lengthy list of movies to make their decision. The novelty of this work is analysis of different movie genres, TV shows genres with accurate results. The presented approach's performance is validated by evaluating various metrics, including precision, recall, mean square error (MSE) and F1-score.</span>