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Journal articles on the topic 'Movie Classification'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Dwivedi, Somya, Harsh Patel, and Shweta Sharma. "Movie Reviews Classification Using Sentiment Analysis." Indian Journal of Science and Technology 12, no. 41 (2019): 1–6. http://dx.doi.org/10.17485/ijst/2019/v12i41/145554.

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Topal, Kamil, and Gultekin Ozsoyoglu. "Emotional classification and visualization of movies based on their IMDb reviews." Information Discovery and Delivery 45, no. 3 (2017): 149–58. http://dx.doi.org/10.1108/idd-05-2017-0045.

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Purpose The purpose of this study is to detect these reviews’ complex emotions, visualize and analyze them. Movie reviewers’ moviescores and reviews can be analyzed with respect to their emotion content, aggregated and projected onto a movie, resulting in an emotion map for a movie. It is then possible for a moviegoer to choose a movie, not only on the basis of movie scores and reviews, but also on the basis of aggregated emotional outcome of a movie as reflected by its emotion map displaying certain emotion map patterns desirable for the moviegoer. Design/methodology/approach The authors use

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Sabrina, Fira, and Winanda Aghniya Puteri. "analysis of the onomatopoeia in “COCO” movie." LADU: Journal of Languages and Education 2, no. 4 (2022): 147–51. http://dx.doi.org/10.56724/ladu.v2i4.101.

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Background: when people watch movies, they not only enjoy the pictures and the action but also related to linguistic expression. One of them is onomatopoeia, which makes dialogue in the film becomes more interesting. Purpose: This research aims to describe kind of onomatopoeic words used in the COCO movie. Design and methods: This method of this research was a descriptive qualitative research. The data were analyzed for collecting the primary and secondary onomatopoeia. In collecting the data for the analysis the source data was taken from the movie entitled “COCO”. In collecting data, at this

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Neetu, Singh, Rajput Rahul, Jaggi Muskan, and Bhatia Rohan. "Movie Genre Classification through Movie posters using Deep Learning techniques." International Journal of Advance and Applied Research 4, no. 32 (2023): 61–65. https://doi.org/10.5281/zenodo.8434380.

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<strong>Abstract</strong><strong>:</strong> The classification of movie genres is a crucial task in the film industry for various purposes, such as recommendation systems, content analysis, and marketing strategies. Traditional methods primarily rely on textual features and metadata, which may not capture the visual essence of a movie accurately. In this research paper, we propose an IMDb genre classifier that utilizes movie posters as input through deep learning techniques. We demonstrate the effectiveness of our approach by training and evaluating a deep neural network on a large-scale datas

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Kadurhalli Sangappa, Jyothi, and Shantala Chikkanaravangala Paramashivaia. "Multi-domain aspect-oriented sentiment analysis for movie recommendations using feature extraction." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1216. http://dx.doi.org/10.11591/ijeecs.v33.i2.pp1216-1223.

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<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 d

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Sangappa, Jyothi Kadurhalli, and Shantala Chikkanaravangala Paramashivaia. "Multi-domain aspect-oriented sentiment analysis for movie recommendations using feature extraction." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 2 (2024): 1216–23. https://doi.org/10.11591/ijeecs.v33.i2.pp1216-1223.

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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 mov

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Narawade, Vaibhav, Aneesh Potnis, Vishwaroop Ray, and Pratik Rathor. "Movie Posters’ Classification into Multiple Genres." ITM Web of Conferences 40 (2021): 03048. http://dx.doi.org/10.1051/itmconf/20214003048.

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Our project intends to classify movies into the three most probable genres that they belong to, from a predefined set of 25 genres, based on only one image i.e the movie poster. We have made use of Convolutional Neural Networks (CNN) to realize this project as we believe it would be of help to extract the features and visual information from the image. Instead of a multi-class classification problem in which the input is classified into any one class, this project would be more correctly described as a multilabel classification problem as a movie belongs to more than one genre. In this project

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Preksha, Khant, and Tidke Bharat. "Multimodal Approach to Recommend Movie Genres Based on Multi Datasets." Indian Journal of Science and Technology 16, no. 30 (2023): 2304–10. https://doi.org/10.17485/IJST/v16i30.1238.

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Abstract <strong>Objectives:</strong> The main purpose of this study is to implement the multimodal as well as the multilabel approach of accurate movie genre classification using the Actor, Director, Writer, Poster Images, and Synopsis data. The various kind of data collected from IMDB as well as existing datasets are used to train the models.<strong> Methods:</strong> The 5 deep learning models are created on Poster Images, Text Data, Actors Data, Directors Data, and Writers Data. These models are then combined using the weighted average ensemble model of deep learning. The fi

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Jhalani, Ritu, Harshita Virwani, Divyanshi Goyal, and Somya Vashishtha. "Prediction of Movie Success Using Classification." Indian Journal of Computer Science 3, no. 6 (2019): 1. http://dx.doi.org/10.17010/ijcs/2018/v3/i6/141443.

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Popat, Anannya, Lakshya Gupta, Gowri Namratha Meedinti, and Boominathan Perumal. "Movie Poster Classification Using Federated Learning." Procedia Computer Science 218 (2023): 2007–17. http://dx.doi.org/10.1016/j.procs.2023.01.177.

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Ramadhan, Nur Ghaniaviyanto, and Teguh Ikhlas Ramadhan. "Analysis Sentiment Based on IMDB Aspects from Movie Reviews using SVM." Sinkron 7, no. 1 (2022): 39–45. http://dx.doi.org/10.33395/sinkron.v7i1.11204.

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A movie is a spectacle that can be done at a relaxed time. Currently, there are many movies that can be watched via the internet or cinema. Movies that are watched on the internet are sometimes charged to watch so that potential viewers before watching a movie will read comments from users who have watched the movie. The website that is often used to view movie comments today is IMDB. Movie comments are many and varied on the IMDB website, we can see comments based on the star rating aspect. This causes users to have difficulty analyzing other users' comments. So, this study aims to analyze th

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Wicaksono, Rafi Anandita, and Erwin Budi Setiawan. "Recommender System Based on Tweets with Singular Value Decomposition and Support Vector Machine Classification." Journal of Computer System and Informatics (JoSYC) 3, no. 4 (2022): 294–302. http://dx.doi.org/10.47065/josyc.v3i4.2072.

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In modern times, the movie industry is growing rapidly. Netflix is one of the platforms that can be used to watch movies and provides many types of genres and movie titles. With so many genres and movie titles sometimes making it difficult for people to choose a movie to watch, one solution to the problem is a recommendation system that can recommend movies based on user ratings. One method in the recommendation system is collaborative filtering. One of the algorithms contained in collaborative filtering is singular value decomposition. Twitter is one of the places where people often write the

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Sulun, Serkan, and Paula Viana. "Movie trailer genre classification using multimodal pretrained features." Expert Systems with Applications 258 (December 15, 2024): 125209. https://doi.org/10.1016/j.eswa.2024.125209.

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Chen, Peng. "Investigating the Impact of Parameter Variations of Transformer Models on Sentiment Classification." Highlights in Science, Engineering and Technology 124 (February 18, 2025): 176–82. https://doi.org/10.54097/qah1rn94.

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With the development of the Internet, an increasing number of applications and websites appearing on the Internet allow movie viewers to add comments for movies. If people want to know what per cent of comments are positive or negative, it will cost a large amount human resources and time to check each comment. However, with the help of the transformer model, it will save a large number of human resources and time to finish sentiment classification for long movie comments. The dataset ‘IMDb’ used to train the transformer model is a large Movie Review Dataset for binary sentiment classification

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SHRUTI, PANT. "SENTIMENT ANALYSIS USING FEATURE SELECTION AND CLASSIFICATION ALGORITHMS." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 7 (2017): 5–11. https://doi.org/10.5281/zenodo.1459090.

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<strong>Here we present a technique to compute the sentiments of movie review dataset so t hat the overall performance of the model is optimised. This model is certain to train and test the model and find the performance constraints. We first pre - process the dataset followed by feature selection and then we will classify the features to investigate the performance. A textual movie review is important as it reveals strong and weak points of the movie plot and by doing the deeper analysis of a movie review one can tell if movie will meet the expectations of the reviewer.</strong> <strong>https

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Ullah, Kifayat, Anwar Rashad, Muzammil Khan, Yazeed Ghadi, Hanan Aljuaid, and Zubair Nawaz. "A Deep Neural Network-Based Approach for Sentiment Analysis of Movie Reviews." Complexity 2022 (June 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/5217491.

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The number of comments/reviews for movies is enormous and cannot be processed manually. Therefore, machine learning techniques are used to efficiently process the user’s opinion. This research work proposes a deep neural network with seven layers for movie reviews’ sentiment analysis. The model consists of an input layer called the embedding layer, which represents the dataset as a sequence of numbers called vectors, and two consecutive layers of 1D-CNN (one-dimensional convolutional neural network) for extracting features. A global max-pooling layer is used to reduce dimensions. A dense layer

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Liu, Chang, Armin Shmilovici, and Mark Last. "Towards story-based classification of movie scenes." PLOS ONE 15, no. 2 (2020): e0228579. http://dx.doi.org/10.1371/journal.pone.0228579.

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Gustary, Devian Try. "Speech Act Analysis In Zootopia Movie." Jurnal Pendidikan Bahasa 11, no. 1 (2022): 153–66. http://dx.doi.org/10.31571/bahasa.v11i1.4176.

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This study aims to investigate the use of speech act and its possible purpose performed by two main characters, Judy Hopps and Nick Wilde in the Zootopia movie. Qualitative method was utilized to explain the function of speech act performed by the characters. Searle’s theory is used as the main theory to answer the two problems formulation regarding speech act classifications and their purposes of using speech act. The research result showed that all of the speech acts was used by Hopps. On the other hand, Wilde used just half of the speech act classification, namely directive, expressive, and

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Benlahbib, Abdessamad, and El Habib Nfaoui. "MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1613. http://dx.doi.org/10.11591/ijece.v11i2.pp1613-1626.

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Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining

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Abdessamad, Benlahbib, and Habib Nfaoui El. "MTVRep: A movie and TV show reputation system based on fine-grained sentiment and semantic analysis." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1613–26. https://doi.org/10.11591/ijece.v11i2.pp1613-1626.

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Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion

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Salsabila, Alifia Salwa, Christy Atika Sari, and Eko Hari Rachmawanto. "Classification of Movie Recommendation on Netflix Using Random Forest Algorithm." Advance Sustainable Science Engineering and Technology 6, no. 3 (2024): 02403016. http://dx.doi.org/10.26877/asset.v6i3.676.

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Netflix is one of the most popular streaming platforms in this world. So many movies and shows with various genres and production countries are available on this platform. Netflix has their own recommendation systems for the subscribers according to their data and algorithm. This research aims to compare two methods of data classifications using Decision Tree and Random Forest algorithm and make a recommendation system based on Netflix dataset. This paper use feature importance to selecting relevant feature and how n_estimators affect the classification. In this research, Random Forest with 50

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Rao, Raghavendra, Aakash A G, Kamalakannan R, Deepika C, and Dinakar D J. "Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System." International Journal of Innovative Research in Information Security 09, no. 03 (2023): 152–55. http://dx.doi.org/10.26562/ijiris.2023.v0903.20.

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Movies are an important part of our daily entertainment, with the global movie industry experiencing significant growth and capturing the attention of people of all ages. However, only a few movies achieve success, leading to pressure on movie production stakeholders. Therefore, researchers and moviemakers require expert systems that can accurately predict the probability of a movie's success prior to its production. Most research on predicting movie popularity has focused on post-production stages, but it's essential to predict a movie's success at an early stage to enable necessary changes t

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N., Suresh Kumar, and Praveena Pothina. "Evolution of hybrid distance based kNN classification." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 510–18. https://doi.org/10.11591/ijai.v10.i2.pp510-518.

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The evolution of classification of opinion mining and user review analysis span from decades reaching into ubiquitous computing in efforts such as movie review analysis. The performance of linear and non-linear models are discussed to classify the positive and negative reviews of movie data sets. The effectiveness of linear and non-linear algorithms are tested and compared in-terms of average accuracy. The performance of various algorithms is tested by implementing them on internet movie data base (IMDB). The hybrid kNN model optimizes the performance classification interns of accuracy. The ac

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Su, Sichang. "Sentimental Analysis Applied on Movie Reviews." Journal of Education, Humanities and Social Sciences 3 (September 22, 2022): 188–95. http://dx.doi.org/10.54097/ehss.v3i.1685.

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Nowadays, Natural Language Processing has received the widespread attention from the natural sciences, and sentimental analysis is one of the most widely used NLP applications. In the age of big data, how to find the required information accurately and quickly has become the hotspot of current research. Based on the movie reviews of two movies from the same series, this paper studies the sentimental trend of movies reviews, in order to help the audience obtain a reference for movie choices. Term frequency-Inverse Document Frequency (TF-IDF) algorithm is applied to evaluate the importance of wo

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Prayoga, Imam, Mahendra Dwifebri Purbolaksono, and Adiwijaya Adiwijaya. "Sentiment Analysis on Indonesian Movie Review Using KNN Method With the Implementation of Chi-Square Feature Selection." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 369. http://dx.doi.org/10.30865/mib.v7i1.5522.

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The advancement and development of the internet is used by the people to support various sectors, one of which is the film industry. Nowadays, people can easily access various movies from available sites. This convenience had led to many reviews about a movie that can be obtained easily. This movie review is very influential on the variety of movies. Freedom of expression on the internet, makes the reviews of a movie vary. For this reason, it is necessary to analyze the sentiment of he movie reviews that are positive or negative. In this research, a sentiment analysis model is build using chi-

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Kabul, Muhammad Shiba, and Erwin Budi Setiawan. "Recommender System with User-Based and Item-Based Collaborative Filtering on Twitter using K-Nearest Neighbors Classification." Journal of Computer System and Informatics (JoSYC) 3, no. 4 (2022): 478–84. http://dx.doi.org/10.47065/josyc.v3i4.2204.

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Netflix is one of the most widely used applications for watching movies online. There are various movie titles that can be watched by users, so a recommendation system is needed to help users who feel confused in choosing movie titles. Twitter is a social media used to express ideas, thoughts, and feelings. Not a few Twitter users who conduct movie discussions, with the movie discussion can be converted into a rating that can be used in the recommendation system. Collaborative Filtering is one of the methods of the recommendation system, by recommending based on the similarity between users (u

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Dwi Orizka, Revina, Ayu Oktaviani, and Agus Triyogo. "AN ANALYSIS OF SPEECH ACT USED IN PETER RABBIT MOVIE." E-LINK JOURNAL 8, no. 2 (2022): 150. http://dx.doi.org/10.30736/ej.v8i2.479.

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This study aimed to find out and describe the forms of speech act used in Peter Rabbit Movie. The method used qualitative design with descriptive method. Data collection techniques in this study was using the human instrument. While human instrument meant data collected by the writer itself. Techniques for analyzing the data with step by step: watched the movie, found out the speech act in dialogue of the movie, captured and identification the dialogues, and noted down the data based on the classification. The result showed, in Peter Rabbit movie the writer found 4 out of 5 classifications. Th

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Li, Lu, Jin Lin, and Tongyu Li. "Movie genre prediction based on the bidirectional encoder representations from transformer." Applied and Computational Engineering 47, no. 1 (2024): 232–37. http://dx.doi.org/10.54254/2755-2721/47/20241383.

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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 e

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Susanto, Angelina. "The Illocutionary and Perlocutionary Acts Produced by The Main Characters of Moxie Movie." k@ta kita 10, no. 3 (2022): 564–70. http://dx.doi.org/10.9744/katakita.10.3.564-570.

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Speech acts are crucial in establishing and achieving a conversation's goal. The purpose of this research is to examine speech acts in the Moxie movie. This study examines types of illocutionary acts and perlocutionary acts produced by the main characters of the Moxie movie, Vivian and Claudia. The writer used two different theories to conduct this research: the theory of speech act by Austin (1962, as cited in Cutting, 2002) and the classification of illocutionary act by Searle (1975, as cited in Cutting, 2002) and Leech (1983, as cited in Peccei, 1999). This study was done using a qualitativ

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Ceyhan, Migena, Zeynep Orhan, and Dimitrios Karras. "An Approach for Movie Review Classification in Turkish." European Journal of Formal Sciences and Engineering 4, no. 2 (2021): 56. http://dx.doi.org/10.26417/328uno67t.

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Web 2.0 has given to all people the right to become a representative of a huge cast of informal media. The importance of this power is getting more evident everyday. Every social media actor can influence the rest of the world by one’s own opinions, feelings, and thoughts generously shared on multiple media. This information belonging to various fields of life can be very handy and be used to one’s advantage, gaining precious experience. One of the greatest problems that this poses is the huge number of data spread everywhere, which are difficult to process as row data per se. Social media and

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Hasan, Zohaib, Abhishek Singh, Vishal Paranjape, and Saurabh Sharma. "Exploring Naive Bayes for Movie Review Sentiment Classification." International Journal of Innovative Research in Computer and Communication Engineering 9, no. 11 (2023): 9930–35. http://dx.doi.org/10.15680/ijircce.2021.0911051.

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This study investigates the performance of Naive Bayes and Logistic Regression classifiers in sentiment analysis of movie reviews using two feature extraction methods: Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). We utilized a dataset of 50,000 IMDB reviews, preprocessed through denoising, stop word removal, and stemming. The reviews were then vectorized using BoW and TF-IDF techniques. Our analysis reveals that Logistic Regression outperforms Naive Bayes in terms of accuracy, with Logistic Regression achieving 89.52% accuracy for BoW and 89.23% for TF-IDF, while

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Ahn, Kwang-Mo, Yun-Suk Kim, Young-Hoon Kim, and Young-Hoon Seo. "Sentiment Classification of Movie Reviews using Levenshtein Distance." Journal of Digital Contents Society 14, no. 4 (2013): 581–87. http://dx.doi.org/10.9728/dcs.2013.14.4.581.

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Supriya, K. C., and P. T. Bharathi. "Movie Classification Based on Genre Using Map Reduce." International Journal of Applied Research on Information Technology and Computing 11, no. 1 (2020): 27. http://dx.doi.org/10.5958/0975-8089.2020.00004.4.

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Janane, S. K. 1. Keerthana M. S. 1. Subbulakshmi B. *1. "HYBRID CLASSIFICATION FOR SENTIMENT ANALYSIS OF MOVIE REVIEWS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 724–28. https://doi.org/10.5281/zenodo.1228816.

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Internet has provided people a platform to express their opinions and thoughts. Sentiment analysis helps to analyse those opinions and categorize them. This research is done on the movie review dataset obtained from the Internet Movie Database (IMDb). The data is classified using some of the popular learning based classifiers like Naive Bayes, Decision Tree and Support Vector Machine (SVM) classifiers and their accuracies are compared. Finally, the three learning based classifiers are combined using the Majority vote ensemble classifier. It is found that the accuracy obtained from the above sa

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Soykök, Irmak Türköz, and H. Altay Güvenir. "Multi-label multi-modal classification of movie scenes." Knowledge-Based Systems 318 (June 2025): 113459. https://doi.org/10.1016/j.knosys.2025.113459.

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Khan, Atif, Muhammad Adnan Gul, M. Irfan Uddin, et al. "Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics." Scientific Programming 2020 (August 1, 2020): 1–14. http://dx.doi.org/10.1155/2020/5812715.

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Information is exploding on the web at exponential pace, so online movie review is becoming a substantial information resource for online users. However, users post millions of movie reviews on regular basis, and it is not possible for users to summarize the reviews. Movie review classification and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is demanded to summarize the vast amount of movie reviews, and it will allow the users to speedily distinguish the positive and negative aspects of a movie. This study has proposed an appro

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Kumar, N. Suresh, and Pothina Praveena. "Evolution of hybrid distance based kNN classification." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 510. http://dx.doi.org/10.11591/ijai.v10.i2.pp510-518.

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<span id="docs-internal-guid-b63d466d-7fff-f94f-7540-9cb92d7bb505"><span>The evolution of classification of opinion mining and user review analysis span from decades reaching into ubiquitous computing in efforts such as movie review analysis. The performance of linear and non-linear models are discussed to classify the positive and negative reviews of movie data sets. The effectiveness of linear and non-linear algorithms are tested and compared in-terms of average accuracy. The performance of various algorithms is tested by implementing them on internet movie data base (IMDB). The

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Rifki, Muhammad Haidar, Yustina Retno Wahyu Utami, and Paulus Harsadi. "TEXT MINING UNTUK ANALISIS SENTIMEN REVIEW FILM MENGGUNAKAN ALGORITMA NAÏVE BAYES." JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi 7, no. 2 (2024): 77–86. http://dx.doi.org/10.32524/jusitik.v7i2.1168.

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In its development, information technology brought about many changes and advances in the context of everyday life. One of the benefits of information technology to process large amounts of data is text mining. A movie is a spectacle that can be done at a relaxed time. Currently, there are many movies that can be watched via the internet or cinema. Movies that are watched on the internet are sometimes charged to watch so that potential viewers before watching a movie will read comments from users who have watched the movie. Film business and its individual reviews cannot be separated and film

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Shaukat, Faheem, Naveed Ejaz, Zeeshan Ashraf, Mrim M. Alnfiai, Nouf Nawar Alotaibi, and Salma Mohsen M. Alnefaie. "An interpretable multi-transformer ensemble for text-based movie genre classification." PeerJ Computer Science 11 (June 25, 2025): e2945. https://doi.org/10.7717/peerj-cs.2945.

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Multi-label movie genre classification is challenging due to the inherent ambiguity and overlap between different genres. Most of the existing works in genre classification use audio-visual modalities. The potential of text-based modalities in movie genre classification is still underexplored. This paper proposes an ensemble deep-learning model that uses movie plots to predict movie genres. After pre-processing the text plots, three transformer-based models, Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, and Robustly Optimized BERT Pre-training Approach (ROBERTa),

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Govindaswamy, Kumar, and Shriram Ragunathan. "Genre Classification of Telugu and English Movie Based on the Hierarchical Attention Neural Network." International Journal of Intelligent Engineering and Systems 14, no. 1 (2021): 54–62. http://dx.doi.org/10.22266/ijies2021.0228.06.

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Genre Classification of movies is useful in the movie recommendation system for video streaming applications like Amazon, Netflix, etc. The existing methods used either video or audio data as input that requires more computation resources to process the data for the genre classification of movies. In this study, the Hierarchical Attention Neural Network (HANN) is proposed for genre classification of movies based on the social media called Twitter data as input. Twitter data related to the Telugu and English movies are collected and applied to HANN for movie’s genre classification. IMDB data ar

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Putri Asykin, Bela Antalia, Suyadi Suyadi, and Efa Silfia. "An Analysis of Illocutionary Act in “Ratatouille” A Movie by Brad Brid." JELT: Journal of English Language Teaching 5, no. 2 (2021): 175. http://dx.doi.org/10.33087/jelt.v5i2.93.

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This study aimed to find out the type of illocutionary acts that is contained the utterance of the main character and to determine the most dominant illocutionary act that is used by the main character in the movie. This research use applies qualitative method. As the result there are 48 utterances that contain illocutionary act found in Ratatouille movie. The researcher found 4 classifications of illocutionary act by using Yule’s classification. The fourth types are; directives found 20 utterances, expressive found 11 utterances, representatives found 10 utterances and commissive found 6 utte

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Ramswamy, Yogesh. "Classifying User Reviews of Movie Applications using Improved Logistic Regression." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1786–97. https://doi.org/10.22214/ijraset.2025.68593.

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Abstract: In recent years review classification, analysis and prediction are one of the most commonapplications of sentiment analysis. It involves detection of sentiments on the reviews made bythe users on social networking applications through opinion mining.In general,reviews canhave positive, negative or neutral polarity indicators. For classification, the polarity indicatorstake the form of certain words and emotions that readily show the user’s sentiments. Existingworks fall short of producing accurate classification results because of two-class problem thataffects the performance of eval

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Jurana, Jurana, and Abdullah Abdullah. "THE SOCIAL STRATIFICATION IN SNOWPIERCER MOVIE." ELITERATE : Journal of English Linguistics and Literature Studies 2, no. 2 (2023): 1. https://doi.org/10.26858/eliterate.v2i2.44017.

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Social stratification is the classification of people into classes that can be arranged in stages. Social stratification can also be referred to as layers between communities that classify individuals and groups who have differences. Vertical hierarchical classification of society resulted in the emergence of social classes so that the terms upper social class, middle class, and lower class emerged. This research will examine (1) how social stratification is described in the snowpiercer movie and also (2) what is the impact of social class classification in the snowpiercer movie. The research

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Chatterjee, Shuvamoy, Kushal Chakrabarti, Avishek Garain, Friedhelm Schwenker, and Ram Sarkar. "JUMRv1: A Sentiment Analysis Dataset for Movie Recommendation." Applied Sciences 11, no. 20 (2021): 9381. http://dx.doi.org/10.3390/app11209381.

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Nowadays, we can observe the applications of machine learning in every field, ranging from the quality testing of materials to the building of powerful computer vision tools. One such recent application is the recommendation system, which is a method that suggests products to users based on their preferences. In this paper, our focus is on a specific recommendation system called movie recommendation. Here, we make use of user reviews of movies in order to establish a general outlook about the movie and then use that outlook to recommend that movie to other users. However, a huge number of avai

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Ramadhanti, Salsabila Baiqlis, Evert Haryanto Hilman, and Maftuchah Dwi Agustina. "TRANSLATION TECHNIQUES AND QUALITY OF SPEECH ACTS FOUND IN A WRINKLE IN TIME MOVIE." IdeBahasa 4, no. 1 (2022): 1–14. http://dx.doi.org/10.37296/idebahasa.v4i1.76.

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This research examined the classification of speech acts, the classification of translation techniques used by the translator in transferring speech acts, and the translation quality found in A Wrinkle in Time movie. Descriptive qualitative method used in this research. The source data was taken from English script and Indonesian subtitles of A Wrinkle in Time (2018) movie using Yule, Molina and Albir, and Nababan et al.’s theories. Based on the results, there were five classifications of speech acts that found in 108 data; representative 46 data (42.6%), expressive 27 data (25.0%), directive

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Liu, Anran. "The Influence and Fusion of Online Films with Traditional Cinema: A Case Study of the Netflix Platform." Communication, Society and Media 6, no. 4 (2023): p1. http://dx.doi.org/10.22158/csm.v6n4p1.

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The emergence and popularization of streaming movies have witnessed the change in acceptance mode and acceptance psychology of traditional movie and television, and broke the confinement of time and space. Taking Netflix, a streaming online platform, as a case study, this research endeavors to explore the impact of streaming movies on traditional cinema movies and their convergence utilizing literature analysis, classification and comparative analysis, case study research method, and data collection and analysis method.

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Shieh, Hwai-Shuh, and Szu-Yu Lin. "A study of the relationship between online movie reviews and the intention to watch the movie." Journal of Economics and Management 44 (2022): 344–75. http://dx.doi.org/10.22367/jem.2022.44.14.

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Aim/purpose – This study explores how the content feature and source of eWOM affect people’s intentions and further analyses the effectiveness of eWOM on people’s inten- tion to watch movies. Design/methodology/approach – The study considers two dimensions of movie reviews, including the source (anonymous or acquaintance) and the content feature (con- crete or abstract), adopts a 2x2 between-subject design, and then analyzes online ques- tionnaires (N = 313) via statistics analysis methods. Findings – The findings showed that if the source is from an acquaintance and the con- tent feature is c

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Reddy. V., Siva RamaKrishna, D. V. L. N. Somayajulu, and Ajay R. Dani. "Classification of Movie Reviews Using Complemented Naive Bayesian Classifier." International Journal of Intelligent Computing Research 2, no. 3 (2011): 148–53. http://dx.doi.org/10.20533/ijicr.2042.4655.2011.0019.

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Kim, Sang-Do, Seong-Bae Park, Se-Young Park, Sang-Jo Lee, and Kweon-Yang Kim. "A Syllable Kernel based Sentiment Classification for Movie Reviews." Journal of Korean Institute of Intelligent Systems 20, no. 2 (2010): 202–7. http://dx.doi.org/10.5391/jkiis.2010.20.2.202.

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Zhai, Y., Z. Rasheed, and M. Shah. "Semantic classification of movie scenes using finite state machines." IEE Proceedings - Vision, Image, and Signal Processing 152, no. 6 (2005): 896. http://dx.doi.org/10.1049/ip-vis:20045178.

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