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Journal articles on the topic 'Movies'
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1
Adikara, Putra Pandu, Yuita Arum Sari, Sigit Adinugroho, and Budi Darma Setiawan. "Movie recommender systems using hybrid model based on graphs with co-rated, genre, and closed caption features." Register: Jurnal Ilmiah Teknologi Sistem Informasi 7, no. 1 (January 30, 2021): 31. http://dx.doi.org/10.26594/register.v7i1.2081.
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A movie recommendation is a long-standing challenge. Figuring out the viewer’s interest in movies is still a problem since a huge number of movies are released in no time. In the meantime, people cannot enjoy all available new releases or unseen movies due to their limited time. They also still need to choose which movies to watch when they have spare time. This situation is not good for the movie business too. In order to satisfy people in choosing what movies to watch and to boost movie sales, a system that can recommend suitable movies is required, either unseen in the past or new releases. This paper focuses on the hybrid approach, a combination of content-based and collaborative filtering, using a graph-based model. This hybrid approach is proposed to overcome the drawbacks of combination in the content-based and collaborative filtering. The graph database, Neo4j is used to store the collaborative features, such as movies with its genres, and ratings. Since the movie’s closed caption is rarely considered to be used in a recommendation, the proposed method evaluates the impact of using this syntactic feature. From the early test, the combination of collaborative filtering and content-based using closed caption gives a slightly better result than without closed caption, especially in finding similar movies such as sequel or prequel.
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Graham, Matthew. "Grid movies." Journal of Knot Theory and Its Ramifications 23, no. 08 (July 2014): 1450038. http://dx.doi.org/10.1142/s0218216514500382.
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We present a grid diagram analogue of Carter, Rieger and Saito's smooth movie theorem. Specifically, we give definitions for grid movies, grid movie isotopies and present a definition of grid planar isotopy as a particular subset of the grid diagram moves: stabilization, destabilization and commutation. We show that grid planar isotopy classes are in one-to-one correspondence with smooth planar isotopy classes by using a new planar grid algorithm that takes a smooth knot diagram to a grid diagram. We then present generalizations of both the smooth and grid movie theorems that apply to surfaces with boundary.
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Eklund, Joshua, and Jong-Min Kim. "Examining Factors That Affect Movie Gross Using Gaussian Copula Marginal Regression." Forecasting 4, no. 3 (July 21, 2022): 685–98. http://dx.doi.org/10.3390/forecast4030037.
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In this research, we investigate the relationship between a movie’s gross and its budget, year of release, season of release, genre, and rating. The movie data used in this research are severely skewed to the right, resulting in the problems of nonlinearity, non-normal distribution, and non-constant variance of the error terms. To overcome these difficulties, we employ a Gaussian copula marginal regression (GCMR) model after adjusting the gross and budget variables for inflation using a consumer price index. An analysis of the data found that year of release, budget, season of release, genre, and rating were all statistically significant predictors of movie gross. Specifically, one unit increases in budget and year were associated with an increase in movie gross. G movies were found to gross more than all other kinds of movies (PG, PG-13, R, and Other). Movies released in the fall were found to gross the least compared to the other three seasons. Finally, action movies were found to gross more than biography, comedy, crime, and other movie genres, but gross less than adventure, animation, drama, fantasy, horror, and mystery movies.
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CARTER, J. SCOTT, and MASAHICO SAITO. "REIDEMEISTER MOVES FOR SURFACE ISOTOPIES AND THEIR INTERPRETATION AS MOVES TO MOVIES." Journal of Knot Theory and Its Ramifications 02, no. 03 (September 1993): 251–84. http://dx.doi.org/10.1142/s0218216593000167.
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A movie description of a surface embedded in 4-space is a sequence of knot and link diagrams obtained from a projection of the surface to 3-space by taking 2-dimensional cross sections perpendicular to a fixed direction. In the cross sections, an immersed collection of curves appears, and these are lifted to knot diagrams by using the projection direction from 4-space. We give a set of 15 moves to movies (called movie moves) such that two movies represent isotopic surfaces if and only if there is a sequence of moves from this set that takes one to the other. This result generalizes the Roseman moves which are moves on projections where a height function has not been specified. The first 7 of the movie moves are height function parametrized versions of those given by Roseman. The remaining 8 are moves in which the topology of the projection remains unchanged.
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Lee, Changjun, and Sung Wook Ji. "Strategies for launching streaming content: Assessing movie-country relatedness and its impact on international popularity." PLOS ONE 19, no. 6 (June 14, 2024): e0305433. http://dx.doi.org/10.1371/journal.pone.0305433.
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The study proposes a way to measure the relatedness between countries and movies (movie—country relatedness density) and to test whether this relatedness is an important factor in predicting the popularity of a movie in a certain country. The results show that both movie—country relatedness density and movie ubiquity (i.e., popularity across many countries) are positively associated with a movie appearing in a country’s top 20 list even after considering other covariates. Based on these findings, we suggest an OTT (Over-the-tops) movie launching strategy with regard to movie—country relatedness density for both global and local OTT companies. Our study contributes to a growing body of research on movie consumption patterns and provides insights into the factors that determine a movie’s success on a global scale. The importance of cultural similarity in adopting films and television shows is widely recognized, but the concept of movie—country compatibility is introduced to take into account the content attributes that cannot be explained by cultural differences. The results suggest that movies that are related to other popular movies in a given country and movies that are popular across many countries are more likely to appear in a country’s top 20 list.
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Ponnamma Divakaran, Pradeep Kumar, and Sladjana Nørskov. "Are online communities on par with experts in the evaluation of new movies? Evidence from the Fandango community." Information Technology & People 29, no. 1 (March 7, 2016): 120–45. http://dx.doi.org/10.1108/itp-02-2014-0042.
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Purpose – The purpose of this paper is to investigate two questions. First, are movie-based online community evaluations (CE) on par with film expert evaluations of new movies? Second, which group makes more reliable and accurate predictions of movie box office revenues: film reviewers or an online community? Design/methodology/approach – Data were collected from a movie-based online community Fandango for a 16-month period and included all movies released during this time (373 movies). The authors compared film reviewers’ evaluations with the online CE during the first eight weeks of the movie’s release. Findings – The study finds that community members evaluate movies differently than film reviewers. The results also reveal that CE have more predictive power than film reviewers’ evaluations, especially during the opening week of a movie. Research limitations/implications – The investigated online community is based in the USA, hence the findings are limited to this geographic context. Practical implications – The main implication is that film studios and movie-goers can rely more on CE than film reviewers’ evaluation for decision making. Online CE can help film studios in negotiating with distributors, theatre owners for the number of screens. Also, community reviews rather than film reviewers’ reviews are looked upon by future movie-goers for movie choice decisions. Originality/value – The study makes an original contribution to the motion picture performance research as well as to the growing research on online consumer communities by demonstrating the predictive potential of online communities with regards to evaluations of new movies.
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Nofrian, Ahmad, and Ira Maisarah. "Analysis of the Moral Values of the One Piece Anime Movie "Z" And Movie ‘’Stampede’’." Journal of English for Specific Purposes in Indonesia 3, no. 1 (January 28, 2024): 24–34. http://dx.doi.org/10.33369/espindonesia.v3i1.27991.
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A movie is a type of literary work to entertain and convey a value or message to the viewer. The movie strives to communicate moral values, which are considered positive attitudes that could be adopted by everyone. This study aims to identify and analyze the moral values contained in the one-piece anime movies "Z" and "Stampede”. This study uses a grounded theory approach to the descriptive-qualitative method and analyze the moral values in the movies by using Sulistiyani’s moral theories. This study focuses on analyzing two anime films from the One Piece series, namely Z and Stampede. This research findings shows that numerous morals can drive and teach us as viewers. The morals found in the movies are courage, self-sacrifice, honesty, justice, wisdom, respect and appreciation, hard work, fulfilling promises, supporting others, and working together. Unfortunately, belief in God's values is not found in these movies since the themes of these movies are action, adventure, and, most significantly, friendship. Moreover, the moral values mostly delivered in these movies are helping others. Finally, the researcher suggests that the viewers not only derive enjoyment from the movie's plot but also apply its moral values to enhance their own character to become better individuals.
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Tang, Jinju. "Cultural Inheritance and Dissemination of Movies: Taking China´s movies as an Example." International Journal of Education and Humanities 12, no. 1 (January 15, 2024): 6–10. http://dx.doi.org/10.54097/h80mtt62.
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This article takes China's movies as an example to explore the cultural inheritance and dissemination of movie. Firstly, the characteristics and value of movies as cultural symbols were introduced, emphasizing the ability of movies as a powerful medium of communication to influence culture. Then an analysis was conducted on the cultural inheritance and dissemination of China's movies, including the historical and developmental characteristics of China's movies, as well as the cultural elements and meanings in China's movies. Subsequently, the inheritance and innovation of Chinese movie culture were discussed, including the presentation and inheritance of traditional Chinese culture in movies, as well as the impact of contemporary social changes on China's movies. Then, specific movie cases were used to analyse the inheritance and dissemination of Chinese movie culture, including the movies "Farewell My Concubine" and "Crouching Tiger, Hidden Dragon". Finally, strategies for the inheritance and dissemination of Chinese movie culture were proposed, including improving the quality of movie creation, perfecting the movie industry system, reforming the movie review system, strengthening international cooperation and exchange, in order to enhance the international status of China´s movies.
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Li, Bo, Yibin Liao, and Zheng Qin. "Precomputed Clustering for Movie Recommendation System in Real Time." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/742341.
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A recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc.) and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.
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Azza, Sekar Yolanda. "THE TRADITIONAL GENDER ROLES STEREOTYPES AS SEEN IN TROLLS (2016) THE DREAMWORKS ANIMATION MOVIE." SIGEH ELT : Journal of Literature and Linguistics 2, no. 2 (September 8, 2022): 91–105. http://dx.doi.org/10.36269/sigeh.v2i2.518.
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In recent years, literature is not merely about poetry, prose, novel, short story but also the movie. One of the exciting animation movies is Trolls, produced by DreamWorks Animation in 2016. Undoubtedly, movies are not always about entertainment but can persuasively create an ideology in the kids' minds. Gender stereotypes are a negative impact of ideology that exists all over the world. Two research questions are formulated; what are the stereotypes towards women in Trolls movie and what ideology the movie seems to promote. This research aims to reveal the traditional stereotype towards women in Trolls movies and make the audience aware of the ideology of gender stereotypes towards women. This research applies library research with the descriptive qualitative method. Since this research analyzes gender stereotypes towards women in the movie, binary opposition theory can see the movie's structure. The writer concludes that women’s traditional gender roles stereotypes still exist in this movie. The stereotypes over women through Poppy as the character can be seen as irrational, weak, and submissive. The researcher also can find the patriarchal ideology. The movie promotes the gender issue in this film that shows the man is superior to the woman
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Zhbankova, E. V. "Soviet Family as Depicted on Screens in 1920s – Authentic or Not?" Concept: philosophy, religion, culture, no. 3 (November 17, 2019): 113–23. http://dx.doi.org/10.24833/2541-8831-2019-3-11-113-123.
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In 2017 it was 90 years since a “cult” movie “The Third Meshanskaya”, by A. Room came out. This movie was known as a “Battleship Potemkin” in everyday life movie genre. In 2018, in turn, it was a 20-year anniversary of the movie “Retro for three”, a remake of “The Third Meshanskaya” by P. Todorovsky.Because of this, students of faculty of foreign languages and area studies were given an assignment as a part of a course “Russian Cinema” to write reviews on both aforementioned movies. The main goal of the task was not to find out students’ opinion about a “provocative” movie shot in 1920s, but rather to assess their knowledge of peculiarities and problems of interpersonal and family relations that people had in the first years of Soviet regime.The results of the experiment were predictable. Students generally liked both movies, especially “The Third Meshanskaya”. Students were surprised both with topicality of the movie’s theme and with the audacity of the film director. It turned out that the realities of soviet life in 1920s are unfamiliar and practically unknown to modern students.This article makes an attempt to address gender issues related to NEP (new economic policy) period of Soviet history in order to define exactly how authentically the daily life of average soviet people was shown in soviet movies. To make an objective assessment of a content of such movies additional sources and materials are required.
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Mohanty, Saurav, Nicolle Clements, and Vipul Gupta. "Investigating the Effect of eWOM in Movie Box Office Success Through an Aspect-Based Approach." International Journal of Business Analytics 5, no. 1 (January 2018): 1–15. http://dx.doi.org/10.4018/ijban.2018010101.
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This study examines the influence of Electronic Word of Mouth (eWOM) on the box office revenue generation of movies in the U.S domestic market using the technique of Aspect-Based Sentiment Analysis (ABSA) and aspect identification. The analysis was conducted on the sentiment score and frequency of five movie aspects from the user reviews collected from high grossing 2014 movies. This study revealed a significant dependence on the aspect-based sentiment frequency of the movie's Story aspect. Surprisingly, the data also showed a strong dependence of movie success on the negative sentiment frequency on the Casting aspect. The findings of the study suggest that the eWOM present in online movie reviews can be used to predict the performance of a movie at the box office by monitoring the aspect's frequency of sentiment, which can be referred to as a metric of the online “buzz” of the movie.
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Lee, Sangjae, and Joon Yeon Choeh. "Movie Production Efficiency Moderating between Online Word-of-Mouth and Subsequent Box Office Revenue." Sustainability 12, no. 16 (August 14, 2020): 6602. http://dx.doi.org/10.3390/su12166602.
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The studies are almost nonexistent regarding production efficiency of movies which is determined based on the relationship between movie resources powers (powers of actors, directors, distributors, and production companies) and box office. Our study attempts to examine how efficiency moderates the relationship between eWOM (online word-of-mouth) and revenue, and to show the difference in prediction performance between efficient and inefficient movies. Using data envelopment analysis to suggest efficiency of movies, movie efficiency negatively moderates the effects of review depth and volume on subsequent box office revenue compensating negative effects of smaller box office in previous period while efficiency exert a positive moderating effect on the influences of review rating and the number of positive reviews on revenue. This shows that review depth and volume are affected by the slack of movie resources powers for inefficient movies, and high rating and positive response for efficient movies to affect revenue. The results of decision trees, k-nearest-neighbors, and linear regression analysis based on ensemble methods using eWOM or movie variables indicate that the movies with the inefficient movie resources powers are providing greater prediction performance than movies with efficient movie resources powers. This show that diverse variation in the efficiency of movie resources powers contributes to prediction performance.
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Marasabessy, Syeikha Annisa, and Lucia Lusi Ani Handayani. "Musical Aspects for Empowering the Black Characters in the Movie Get Out (2017)." Resital: Jurnal Seni Pertunjukan 20, no. 2 (August 27, 2019): 70–80. http://dx.doi.org/10.24821/resital.v20i2.2594.
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In this 21st century, the representation of Black people in many U.S. movies is still problematic, for the movies do not omit the stereotypical representations of Black people, which are often depicted being disrespectful and unintelligent compared to other races. Many movies have been trying to change them into another perspective, yet they are still unable to completely get rid of those stereotypes. By looking through the cinematic aspects, the dialogues, and the symbols along with the sounds and music used, this paper examines the stereotypes of Black characters the movie Get Out (2017) by Jordan Peele using discourse analysis. The paper observes that the representation of the movie still distinguishes Black from White in the aspects of body over mind in Black masculinity, incivility, and distinctive racial labor. As a result, Black characters are seen inferior compared to White characters despite the movie’s effort to empower them. The use of music also emphasizes the power relation difference between the two races. Overall finding of the paper reveals that the existence of Black stereotypical depiction is still found in a movie empowering Black people showing that race representation should be monitored thoroughly.
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Dhungana, Jivan. "Film as Mass Communication and its Responsibility to Social Change." Interdisciplinary Journal of Management and Social Sciences 5, no. 1 (February 19, 2024): 58–66. http://dx.doi.org/10.3126/ijmss.v5i1.62663.
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This paper considers movies as means of mass communication and attempts to highlight the challenges faced by Nepali movies while communicating with people. It believes that the movies are key artistic presentations by which movie-mediatization is possible and a significant positive change can be brought. Movies are made as effectively to socialize people as to provide entertain to them. It means that the movies are highly responsible for bringing positive vibes in society. In order look at the manner in which films are giving a message to the audience, this paper makes a review of key literature on movie, mass communication, mediatization, and impact of movie on mediatization, analyzes the impact of movies on society, and forwards some key conclusions on epitomizing women as heroes. It also takes into consideration the significant impact made by some Indian movies to the Nepali movie industry, which is not growing well because of the premiere of recent Indian movies that are superior to Nepali movies both in quality and quantity. To conclude, the paper encapsulates the notion of film as an art, which has easily entered into people's mind and heart, and is changing people's preoccupied mindset on women and gender roles.
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Liu, Xuan, Savannah Wei Shi, Thales Teixeira, and Michel Wedel. "Video Content Marketing: The Making of Clips." Journal of Marketing 82, no. 4 (July 2018): 86–101. http://dx.doi.org/10.1509/jm.16.0048.
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Consumers have an increasingly wide variety of options available to entertain themselves. This poses a challenge for content aggregators who want to effectively promote their video content online through original trailers of movies, sitcoms, and video games. Marketers are now trying to produce much shorter video clips to promote their content on a variety of digital channels. This research is the first to propose an approach to produce such clips and to study their effectiveness, focusing on comedy movies as an application. Web-based facial-expression tracking is used to study viewers’ real-time emotional responses when watching comedy movie trailers online. These data are used to predict both viewers’ intentions to watch the movie and the movie's box office success. The authors then propose an optimization procedure for cutting scenes from trailers to produce clips and test it in an online experiment and in a field experiment. The results provide evidence that the production of short clips using the proposed methodology can be an effective tool to market movies and other online content.
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Gore, Mohini, Aishwarya Sheth, Samrudhi Abbad, Paryul Jain, and Prof Pooja Mishra. "IMDB Box Office Prediction Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2438–42. http://dx.doi.org/10.22214/ijraset.2022.42653.
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Abstract: Movies are a big part of our world! But nobody knows how a movie will perform at the box office. There are some bix budget movies that bomb and there are smaller movies that are smashing successes. This project tries to predict the overall worldwide box office revenue of movies using data such as the movie cast, crew, posters, plot keywords, budget, production companies, release dates, languages, and countries. The dataset on Kaggle contains all these data points that you can use to predict how a movie will fare at the box office. Among many movies that have been released, some generate high profit while the others do not. This paper studies the relationship between movie factors and its revenue and build prediction models. Besides analysis on aggregate data, we also divide data into groups using different methods and compare accuracy across these techniques as well as explore whether clustering techniques could help improve accuracy Keywords: component: regression; predictive analytics; Clustering; Expectation-maximization; K-means; Movies
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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 (June 23, 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 to be made. To this end, a content-based movie recommendation system (RS) has been proposed that uses preliminary movie features such as genre, cast, director, keywords, and movie description. This RS output and movie rating and voting information are then used to create a new feature set, which is input into a CNN deep learning (DL) model to build a multiclass movie popularity prediction system. The study also proposes a system to predict the popularity of the upcoming movie among different age groups, dividing the audience into four categories: junior, teenage, mid-age, and senior. The study uses publicly available data from the Internet Movie Database (IMDb) and The Movie Database (TMDb). The multiclass classification model implemented in this study achieved 96.8% accuracy, outperforming all benchmark models. Overall, this study highlights the potential of predictive and prescriptive data analytics in information systems to support industry decision- making.
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Li, Xiangxiang, and Byeong-Jin Kim. "The Effect of Character Conformity in the Title Logos of Chinese Anti-Japanese War Movies on the Attitudes and Intentions of Viewers - Focus on the Mediating Effect of the Audience's Preference for the Movie Genre." Korea Industrial Technology Convergence Society 28, no. 3 (September 30, 2023): 161–72. http://dx.doi.org/10.29279/jitr.2023.28.3.161.
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This study investigated the impact of typographic suitability of movie titles and logos on movie posters, as well as viewers' preferences for movie genres, on viewers’ attitudes toward and intentions to watch movies. Empirical research results revealed that higher typographic suitability of movie titles and logo fonts on movie posters led to more positive attitudes and intentions toward watching the movies. Furthermore, a significant positive influence of movie genre preferences on movie attitude and intention to watch was observed. Lastly, a meaningful mediating effect was found between typographic suitability of movie posters and viewers’ attitudes and intentions toward watching movies.
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Laustsen, Carsten Bagge. "Film og samfund." Slagmark - Tidsskrift for idéhistorie, no. 58 (March 9, 2018): 127–47. http://dx.doi.org/10.7146/sl.v0i58.104716.
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The article is an investigation of the relation between social studies and movies and in turn the relevance of movies to the social sciences. The relationship between the world of movies and reality is analyzed through the movies Being there and Storytelling. Three different approaches are presented: The movie society, socio-fiction and movies as ambiguos cultural products.
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Xiao, Xingyao, Yihong Cheng, and Jong-Min Kim. "Movie Title Keywords: A Text Mining and Exploratory Factor Analysis of Popular Movies in the United States and China." Journal of Risk and Financial Management 14, no. 2 (February 6, 2021): 68. http://dx.doi.org/10.3390/jrfm14020068.
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Unprecedented opportunities have been brought by advancements in machine learning in the prediction of the economic success of movies. The analysis of movie title keywords is one promising but rarely investigated direction of study. To address this gap, we performed a text mining and exploratory factor analysis (EFA) of the relationships between movie titles and their corresponding movies’ levels of success. Specifically, intragroup and intergroup analyses of 217 top hit movies in the United States and 245 top hit movies in China showed that successful movies in these two major movie markets with outstanding total lifetime grosses featured titles with similar and different patterns of most frequently used words, revealing useful information about viewers’ preferences in these countries. The findings of this study will serve to better inform the movie industry in giving more economically promising names to their products from a machine-learning perspective and inspire further studies.
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Abou Zeid, Dina Farouk. "The Impact of Movies on Tourism among Egyptian Youth." Mediterranean Journal of Social Sciences 12, no. 5 (September 5, 2021): 90. http://dx.doi.org/10.36941/mjss-2021-0047.
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The research study aims to examine the impact of movies on tourism among Egyptian youth by studying the different genres of movies and the different types and forms of tourism besides applying infotainment to discover the elements of information and entertainment in movies which encourage the youth to travel. A survey is conducted of a disproportionate stratified sample of 500 Egyptian university students divided equally between private and public universities and between males and females who have passion for traveling, travel at least once a year and are members of travel Facebook groups. The results show that Egyptian and non-Egyptian movies encourage Egyptian university students to travel abroad. The most popular types of movie-induced tourism among the youth are traveling to destinations portrayed in movies, organized tour of portrayed locations, tour of studio sets and movie-themed park. The findings indicate that movie- induced tourism is affected by movies' infotainment. Received: 22 July 2021 / Accepted: 28 September 2021 / Published: 5 September 2021
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Ng, Yiu-Kai. "MovRec: a personalized movie recommendation system for children based on online movie features." International Journal of Web Information Systems 13, no. 4 (November 6, 2017): 445–70. http://dx.doi.org/10.1108/ijwis-05-2017-0043.
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Purpose The purpose of this study is to suggest suitable movies for children among the various multimedia selections available these days. Multimedia have a significant impact on the social and psychological development of children who are often explored to inappropriate materials, including movies that are either accessible online or through other multimedia channels. Even though not all movies are bad, there are negative effects of offensive languages, violence and sexuality as exhibited in movies. Parents and guidance of children need all the help they can get to promote the healthy use of movies these days. Design/methodology/approach To offer parents appropriate movies of interest to their youths, the authors have developed MovRec, a personalized movie recommender for children, which is designed to provide educational and suitable entertaining opportunities for children. MovRec determines the appealingness of a movie for a particular user based on its children-appropriate score computed by using the backpropagation model, pre-defined category using latent Dirichlet allocation, its predicted rating using matrix factorization and sentiments based on its users’ reviews, which along with its like/dislike count and genres, yield the features considered by MovRec. MovRec combines these features by using the CombMNZ model to rank and recommend movies. Findings The performance evaluation of MovRec clearly demonstrates its effectiveness and its recommended movies are highly regarded by its users. Originality/value Unlike Amazon and other online movie recommendation systems, such as Common Sense Media, Internet Movie Database and TasteKid, MovRec is unique, as to the best of the authors’ knowledge, MovRec is the first personalized children movie recommender.
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Azaki, Muhammad Bilal Rafif, and Z. K. A. Baizal. "Movie Recommender System Using Decision Tree Method." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 8, no. 3 (August 30, 2023): 729–35. http://dx.doi.org/10.29100/jipi.v8i3.3867.
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In this modern era, many things that can be done online, one of which is watching movies. When the number of movies increases, people often find it difficult to decide which movie to watch next. To solve this problem, a useful recommendation system was developed to find movies that one might like based on movies that have been watched before. This research develops a movie recommendation system using Collaborative Filtering (CF) with the Decision Tree algorithm. In this study, the data used were movie data and ratings obtained from the grouplens.org website. Then the movielens dataset is filtered and only saves movies with a rating of more than 50 that are used in the recommendation system. In this study, Mean Absolute Error (MAE) is used as a method to assess the accuracy of the movie recommendation system. Based on the research that has been done, Decision Tree gets better results with an MAE value of 0,942 compared to Collaborative Filtering with an MAE value of 1,242.
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Ahmad, Afaq. "VERSATILITY THY NAME IS AAMIR KHAN: THEMATIC STUDY OF THREE SELECTED FILMS." International Journal of Research -GRANTHAALAYAH 7, no. 4 (April 30, 2019): 213–24. http://dx.doi.org/10.29121/granthaalayah.v7.i4.2019.893.
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Hindi cinema has acquired a universal identity in Indian society as it is one of the most popular forms of entertainment, education and information in India. As purveyor of entertainment, Bollywood has provided a platform to many actors to flourish their talents as an actor. As far as Aamir Khan is concerned, acting is the life, blood, and soul of Aamir Khan. The way he portrays various characters in his movies, make him a perfect actor of versatile genre. Less studies have been conducted on Aamir Khan and his movies, and on the versatile portrayal of Aamir Khan contained in his movies. The purpose of this paper is to understand the versatile characteristics played by Aamir Khan in his movies. The present paper makes an attempt to critically analyse the three movies of different genres of Aamir Khan. These movies were purposively selected as Lagaan – a patriotic, productional and nostalgic movie, Rang De Basanti – a revolutionary, nationalistic and rebellious movie, and Taare Zameen Par – an inspirational, experimental and directorial movie, on account of their distinguishing stories. As the selected films etched a niche in the hearts and minds of cinemagoers and movie viewers due to their innovative stories and splendid cinematography, the thematic analysis was undertaken. It has been found from the study that message-oriented aspects depicted in these movies. Further, the informative, inspirational, patriotic, reformative, and revolutionary characteristics predominantly glimpsed in his movies. The versatile characteristics of Aamir Khan was also meticulously scrutinized. It has been found after having a critical analysis that Aamir Khan has versatile characteristics as depicted in these three selected movies under study.
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Sahu, Sandipan, Raghvendra Kumar, Pathan MohdShafi, Jana Shafi, SeongKi Kim, and Muhammad Fazal Ijaz. "A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews." Mathematics 10, no. 9 (May 6, 2022): 1568. http://dx.doi.org/10.3390/math10091568.
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Movies are one of the integral components of our everyday entertainment. In today’s world, people prefer to watch movies on their personal devices. Many movies are available on all popular Over the Top (OTT) platforms. Multiple new movies are released onto these platforms every day. The recommendation system is beneficial for guiding the user to a choice from among the overloaded contents. Most of the research on these recommendation systems has been conducted based on existing movies. We need a recommendation system for forthcoming movies in order to help viewers make a personalized decision regarding which upcoming new movies to watch. In this article, we have proposed a framework combining sentiment analysis and a hybrid recommendation system for recommending movies that are not yet released, but the trailer has been released. In the first module, we extracted comments about the movie trailer from the official YouTube channel for Netflix, computed the overall sentiment, and predicted the rating of the upcoming movies. Next, in the second module, our proposed hybrid recommendation system produced a list of preferred upcoming movies for individual users. In the third module, we finally were able to offer recommendations regarding potentially popular forthcoming movies to the user, according to their personal preferences. This method fuses the predicted rating and preferred list of upcoming movies from modules one and two. This study used publicly available data from The Movie Database (TMDb). We also created a dataset of new movies by randomly selecting a list of one hundred movies released between 2020 and 2021 on Netflix. Our experimental results established that the predicted rating of unreleased movies had the lowest error. Additionally, we showed that the proposed hybrid recommendation system recommends movies according to the user’s preferences and potentially promising forthcoming movies.
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Wang, Ruijie. "Research on Box Office Influencing Factors and Coping Strategies of Chinese War Genre Movies." Highlights in Business, Economics and Management 28 (April 9, 2024): 130–36. http://dx.doi.org/10.54097/43a6jf94.
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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.
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Utami, Sri, Kemas Muslim Lhaksmana, and Yuliant Sibaroni. "Deep Learning and Imbalance Handling on Movie Review Sentiment Analysis." SinkrOn 8, no. 3 (July 31, 2023): 1894–907. http://dx.doi.org/10.33395/sinkron.v8i3.12770.
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Before watching a movie, people usually read reviews written by movie critics or regular audiences to gain insights about the movie’s quality and discover recommended films. However, analyzing movie reviews can be challenging due to several reasons. Firstly, popular movies can receive hundreds of reviews, each comprising several paragraphs, making it time-consuming and effort-intensive to read them all. Secondly, different reviews may express varying opinions about the movie, making it difficult to draw definitive conclusions. To address these challenges, sentiment analysis using CNN and LSTM models, known for their effectiveness in classifying text in various datasets, was performed on the movie reviews. Additionally, techniques such as TF-IDF, Word2Vec, and data balancing with SMOTEN were applied to enhance the analysis. The CNN achieved an impressive sentiment analysis accuracy of 98.56%, while the LSTM achieved a close 98.53%. Moreover, both classifiers performed well in terms of the F1-score, with CNN obtaining 77.87% and LSTM achieving 78.92%. These results demonstrate the effectiveness of the sentiment analysis approach in extracting valuable insights from movie reviews and helping people make informed decisions about which movies to watch.
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Shishodia, Dinesh. "Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4919–24. http://dx.doi.org/10.22214/ijraset.2021.35929.
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This paper represents the overview of Approaches and techniques used in Movie Recommendation system. Recommendation system is used by many companies like Netflix, Amazon, Flipkart etc. It makes the user experience better and decrease the user efforts. It plays a very vital role in our day-to-day life. It is used in recommending Movies, Articles, News, Books, Music, Videos, People (Online Dating) etc. It learns from the user past behavior and based on that behavior it recommends item to the user. Likewise, in Movie Recommendation system movie is recommended to the user on the basis of movies watched, liked, rated by the user. In year 2020, approximate 10,000 movie were launched according to IDMB data. It saves a lot of times and efforts of the user by suggesting movies according to user taste and user don’t have to select a movie from a large set of movies.
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Pratama, Qaris Ardian, and I. Gede Arta Wibawa. "Sistem Rekomendasi Film Dengan Pendekatan Ontologi." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 12, no. 2 (February 5, 2023): 287. http://dx.doi.org/10.24843/jlk.2023.v12.i02.p06.
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Movie is one of the easiest and cheapest entertainment human can experience. Nevertheless, there is an abundant amount of movies to watch. In the US and Canada alone, there are 403 movies produced in 2021. That is a huge amount of movies one person can watch. Most of people usually confused in determining which movies to watch, especially after watching a movie that truly suits their taste. Determining a decision in choosing a movie to watch requires a recommendation system. The recommendation system will provide decisions with good accuracy if it is collaborated with the Semantic Web using ontologies. In this study, researchers aims to build a movie ontology design which will later be used as a processing database in a movie selection recommendation system. In building the ontology, researcher requires the Methontology method. The methontology stages are performing the stages of specification, knowledge acquisition, conseptualizationo, integration, implementation, evaluation, and documentation.
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Zhang, Yingchun, Jianbo Huang, and Siwen Duan. "3D video conversion system based on depth information extraction." MATEC Web of Conferences 232 (2018): 02048. http://dx.doi.org/10.1051/matecconf/201823202048.
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3D movies have received more and more attention in recent years. However, the investment in making 3D movies is high and difficult, which restricts its development. And there are many existing 2D movie resources, and how to convert it into 3D movies is also a problem. Therefore, this paper proposes a 3D video conversion system based on depth information extraction. The system consists of four parts: segmentation of movie video frame sequences, extraction of frame image depth information, generation of virtual multi-viewpoint and synthesis of 3D video. The system can effectively extract the depth information of the movie and by it finally convert a 2D movie into a 3D movie.
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Tan, Shilin. "Chinese Science Fiction Movies' Innovations and Development Analysis Take the Series of The Wandering Earth as an Example." Communications in Humanities Research 9, no. 1 (October 31, 2023): 128–33. http://dx.doi.org/10.54254/2753-7064/9/20231146.
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With an increasing output of Chinese science fiction movies in recent years, there are many innovative and creative strategies among these movies, taking the series The Wandering Earth as an example. It is considered to be valuable to analyze each new strategy of narrative and filmmaking technology in order to find out the proper ways for the future development of Chinese science fiction movies. Especially for the high competence of the Hollywood movie industry from its monopoly of genre movies, it is full of challenges to surpass the great achievement and influence of Hollywood. This paper is going to provide a summarization of the movie creativity of the series The Wandering Earth by reading literature. These summarized innovations will then be along with appropriate analysis which is targeting to explain their exact influence on filmmaking, the film market, and the audiences. As a result, future developing suggestions for Chinese science fiction movies are presented based on the recent movie industry status quo.
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Chen, Man, Xiaomin Han, Xinguo Zhang, and Feng Wang. "The business model of Chinese movies." Journal of Contemporary Marketing Science 2, no. 3 (December 17, 2019): 246–61. http://dx.doi.org/10.1108/jcmars-02-2019-0015.
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Purpose The motion picture industry is a cultural and creative industry. Unlike its US counterpart, the Chinese motion picture industry is still developing. Therefore, learning from the US market, the purpose of this paper is to analyze the business model of Chinese movies from the perspective of new product diffusion. Design/methodology/approach Based on 66 movies released in the US and 21 movies released in China, this paper first compares the diffusion curves of Chinese and US movies through the movie life cycle and box office trends. Next, it analyzes the moviegoing behaviors of Chinese and US audiences based on the innovation and imitation coefficients in the Bass model. Finally, it compares the attention to information of Chinese and US audiences from the perspective of interpersonal word-of-mouth (WOM). Findings In the USA, a movie’s highest weekly box office is usually in its opening week, followed by a weekly decline in revenue; in China, there is no difference in box office performance between the first two weeks, but a weekly decline in revenue similarly follows. US audiences pay more attention to advertisements for movies than WOM recommendations, while Chinese people pay more attention to WOM recommendations. Neither the Chinese nor the US market differs in the volume of WOM between the first week before release and the opening week, and these two weeks are the most active period of WOM in both markets. Practical implications During the production phase for Chinese movies, we should satisfy opinion leaders’ needs. During the distribution phase, we should not only focus on market spending before the movie’s release, but also increase market spending in the opening week. During the theater release phase, we should stimulate WOM communication between moviegoers and thereby attract many more opinion seekers. Originality/value Few studies have investigated the Chinese motion picture industry from the perspective of new products. This paper compares and analyzes the diffusion of Chinese and US movies using the Bass model of new product diffusion, providing systematic theoretical guidelines for the commercial operation of the Chinese motion picture industry.
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Bekalu, Mesfin Awoke, and K. Viswanath. "Smoking portrayal in Ethiopian movies: a theory-based content analysis." Health Promotion International 34, no. 4 (April 17, 2018): 687–96. http://dx.doi.org/10.1093/heapro/day013.
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Abstract Considerable research from high-income countries has characterized the amount, nature and effects of movie smoking depiction. However, in low- and middle-income countries (LMICs) where tobacco use and tobacco-related diseases are growing, little research has investigated smoking imagery in movies. This study examined the extent and nature of smoking portrayal in locally produced Ethiopian movies, and estimated the number of tobacco impressions movies delivered. Sample movies were taken from YouTube. Keyword searches were conducted using ‘Ethiopian movies’ and ‘Ethiopian drama’ on 18 September 2016. In each search, the first 100 most viewed movies were examined. Excluding repeated results, a total of 123 movies were selected for content analysis. Three coders participated. Results indicated that 86 (69.9%, 95% CI 63–78%) of the 123 most viewed movies contain at least one tobacco incident (TI). The movies depict a total of 403 TIs, with an average of 4.7 (95% CI 3.7–5.6) TIs in each movie. The average length of TIs is 1 min and 11 s. On average, the movies were viewed more than half a million times by September 2016, and received more ‘likes’ than ‘dislikes’, z = −8.05, p = 0.00. They delivered over 194 million tobacco impressions via YouTube alone from July 2012 through September 2016. Most TIs portray smoking as a socially acceptable behavior with no negative health consequences. The findings suggest that as with transnational Western movies, locally produced movies in LMICs should be scrutinized for compliance with national and international regulatory efforts.
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Sukarta, Allexandro Billy, M. Mahaputra Hidayat, and Rifki Fahrial Zainal. "Decision Support System for Movie Recommendations Based on Multi User Preferences Using the Simple Additive Weighting Method." JEECS (Journal of Electrical Engineering and Computer Sciences) 7, no. 2 (January 13, 2023): 1285–92. http://dx.doi.org/10.54732/jeecs.v7i2.22.
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At this time advances in technology and information have experienced rapid progress, one of which is in the field ofentertainment, both audio and visual. And one of the entertainments is movies. With the increasing number of movies,there are several classifications of movie genres to assist users in finding and selecting movies to watch, but the genreclassification itself is still very general. Due to the above factors, especially in genre, subgenre, rating, movie durationwhich always develops over time according to a certain pattern and also audiences who have different moviepreferences, the researcher sees that there is a need for an application that can recommend movies with preferencesthat can be set according to the wishes of movie lovers. From the problems that arise, this research was built using theSimple Additive Weighting (SAW) method which aims to make it easier for users to determine which movie to choose.This system produces a web-based information system using several parameters, namely the main genre of a movie,subgenre, movie rating, movie duration, and year of making.
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Singh, Abhay Pratap, Mukul Sharma, Ashish Chauhan, and Kamal Soni. "Build a Recommendation System for Movies or Books." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1379–82. http://dx.doi.org/10.22214/ijraset.2023.50303.
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Abstract: These days everyone will change their life style to search the movies on the internet. They will provide the information of their preferences which the y like to watch. There are many recommended and popular system is applied to search their favorite things like books, articles music videos, movies etc. these paper we are proposed a movie recommendation system. It will be working on the various filter or collaborate filter method that will collect and gave the information to the user and it will also analyzes the user and gave them the best movies to there users at that time. We sorted the movies according to the user recommendation of the previous users preferences so for these purpose we use k-means algorithm. Movie recommendation system can also help the user to find the choices of there movies are based on there previous experiences and manners without wasting its useless time on the browser to search their best movies. It will give us various type of previous recommendation using customized the database. then the user can browse it easily and choice his best movie
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Malik, Sonika. "Movie Recommender System using Machine Learning." EAI Endorsed Transactions on Creative Technologies 9, no. 3 (October 11, 2022): e3. http://dx.doi.org/10.4108/eetct.v9i3.2712.
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In this research, we propose a movie recommender system that can recommend movies to both new and existing customers. It searches movie databases for all of the relevant data, such as popularity and beauty that is required for a recommendation. We apply both content-based and collaborative filtering and evaluate their advantages and disadvantages. To build a system that delivers more exact movie recommendations, we employ hybrid filtering, which is a combination of the outcomes of these two processes. The recommendation engines are also used for business purposes and to make strategies for organizations. Due to the growing demands of customers and user’s recommendation systems plays a huge role. These recommender systems also help us to utilize our time in the busy world by giving us more relevant searches. These systems are generally used with the movie’s websites or with many commercial applications and are of great use. This type of recommendation system can be also used for precise results. It will make movies suggestions more relevant as per the need of the users.
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Sinha, Shweta, and Treya Sharma. "Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations." International Journal of Innovative Research in Computer Science and Technology 11, no. 3 (May 20, 2023): 67–71. http://dx.doi.org/10.55524/ijircst.2023.11.3.12.
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With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessing of movie metadata from various sources, including genres, actors, directors, and plot summaries. Feature extraction techniques are applied to transform the textual information into meaningful representations that capture the essential characteristics of each movie. Next, a content-based filtering algorithm is employed to compute similarity scores between the user's movie preferences and the extracted features of the available movies. The proposed approach contributes to the advancement of movie recommendation systems and has the potential to enhance user engagement and satisfaction in movie selection.
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Ahmad, Ibrahim Said, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub, and Mohammad Darwich. "Sequel movie revenue prediction model based on sentiment analysis." Data Technologies and Applications 54, no. 5 (October 8, 2020): 665–83. http://dx.doi.org/10.1108/dta-10-2019-0180.
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PurposeSequel movies are very popular; however, there are limited studies on sequel movie revenue prediction. The purpose of this paper is to propose a sentiment analysis based model for sequel movie revenue prediction and to propose a missing value imputation method for the sequel revenue prediction dataset.Design/methodology/approachA sequel of a successful movie will most likely also be successful. Therefore, we propose a supervised learning approach in which data are created from sequel movies to predict the box-office revenue of an upcoming sequel. The algorithms used in the prediction are multiple linear regression, support vector machine and multilayer perceptron neural network.FindingsThe results show that using four sequel movies in a franchise to predict the box-office revenue of a fifth sequel achieved better prediction than using three sequels, which was also better than using two sequel movies.Research limitations/implicationsThe model produced will be beneficial to movie producers and other stakeholders in the movie industry in deciding the viability of producing a movie sequel.Originality/valuePrevious studies do not give priority to sequel movies in movie revenue prediction. Additionally, a new missing value imputation method was introduced. Finally, sequel movie revenue prediction dataset was prepared.
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Yanagi, U., Noriko Kaihara, Dai Simazaki, Kanae Bekki, Yoshinori Homma, Chiemi Iba, Atsuto Asai, and Motoya Hayashi. "Bacterial Flora on Mist Outlet Surfaces in 4D Theaters and Suspended Particle Concentration Characteristics during 4D Movie Screenings." Microorganisms 11, no. 7 (July 22, 2023): 1856. http://dx.doi.org/10.3390/microorganisms11071856.
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In this study, we measured suspended particle concentrations during the screening of 4D movies (3 screens and 15 movies) and 2D movies (9 screens and 9 movies) in 3 movie theaters to obtain a more detailed understanding of the situation of suspended particle concentrations and adherent bacterial flora in 4D movie theaters, which have been introduced in increasing numbers in recent years. The adherent bacterial flora on the floor and mist outlet surfaces in the 4D movie theaters were collected and analyzed. During the movie showings, the concentrations of suspended particles in 4D movie theaters were significantly higher than those in 2D movie theaters (p < 0.001). A significant increase in suspended particle concentrations due to 4D movie effects was also observed. The results of the α-diversity and β-diversity analyses indicate that the bacterial flora on the surfaces of mist outlets in 4D movie theaters are similar. Moreover, there are many closely related species, and the bacterial flora are rich and contain rare bacterial species. Many of the bacterial genera that are dominant in 4D theaters are suited to aqueous environments, and bacteria in the water supply system may have an impact on the indoor environment.
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Klemenc Ketiš, Zalika, and Igor Švab. "Using movies in family medicine teaching: A reference to EURACT Educational Agenda." Slovenian Journal of Public Health 56, no. 2 (June 1, 2017): 99–106. http://dx.doi.org/10.1515/sjph-2017-0013.
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Abstract Introduction Cinemeducation is a teaching method where popular movies or movie clips are used. We aimed to determine whether family physicians’ competencies as listed in the Educational Agenda produced by the European Academy of Teachers in General Practice/Family Medicine (EURACT) can be found in movies, and to propose a template for teaching by these movies. Methods A group of family medicine teachers provided a list of movies that they would use in cinemeducation. The movies were categorised according to the key family medicine competencies, thus creating a framework of competences, covered by different movies. These key competencies are Primary care management, Personcentred care, Specific problem-solving skills, Comprehensive approach, Community orientation, and Holistic approach. Results The list consisted of 17 movies. Nine covered primary care management. Person-centred care was covered in 13 movies. Eight movies covered specific problem-solving skills. Comprehensive approach was covered in five movies. Five movies covered community orientation. Holistic approach was covered in five movies. Conclusions All key family medicine competencies listed in the Educational Agenda can be taught using movies. Our results can serve as a template for teachers on how to use any appropriate movies in family medicine education.
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Wicaksono, Rianto Duta, and Yuli Kuswardani. "Translation analysis of subtitle from English into Indonesian in The Raid 2 Movie." English Teaching Journal : A Journal of English Literature, Language and Education 7, no. 2 (November 24, 2019): 79. http://dx.doi.org/10.25273/etj.v7i2.5439.
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<span>Indonesian movies make foreigners interested to watch it. English subtitle is the solution to make Indonesian movies worldwide. Subtitle’s strategies to make good subtitle is very important to make clear subtitle. The objective of the research are to identify subtitling strategies subtitle used in translating The Raid 2 movie and to describe the subtitling clarity of The Raid 2 movie’s English subtitle. The research method used is descriptive approach. The data are required from three source, they are the script of The Raid 2 movie, the reader evaluation and the expert evaluation. This research uses documentation as the technique of collecting data. The researchers use data, investigator, and theory triangulation to make this research can be trusted. The results of this research show that: (1) there are 11 strategies that subtitle used in translating The Raid 2 movie; (2) the clarity of subtitle in The Raid 2 movie is high. The percentage of subtitling clarity from respondents is 91, 02%. The researchers obtain 41 subtitles included into lowest score, 154 subtitles included into medium score, and 681 subtitles included into high score.</span>
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Safnidar Siahaan, Safnidar. "A COMPARATIVE ANALYSIS OF THE MAIN CHARACTERS’ MANIPULATIONS BETWEEN “THE TALENTED MR.RIPLEY “AND “TO DIE FOR” MOVIES." ANGLO-SAXON: Jurnal Ilmiah Program Studi Pendidikan Bahasa Inggris 12, no. 1 (August 14, 2021): 31–42. http://dx.doi.org/10.33373/as.v12i1.3201.
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This research will analyze one of literary works namely movie. This research will be focused on the manipulation action as the human personality part that can be found in the movie especially about the main characters’ manipulation actions in two movies namely movies entitled “The Talented Mr. Ripley” and “To Die For”, then the writer will compare them. There are two points that will be described relating to the manipulation, those are; manipulation as one of Machiavellianism traits and manipulation as one of the behavior patterns used by narcissists. The purpose of this research was to describe the similarities and differences of both movies through the manipulation action from the both movies. In this research, the writer used the descriptive qualitative method. Furthermore, the writer collects the data from the two movies to classify the similarities and differences refer to manipulation action of the main characters. The data collection in this research was done by watching, and took notes before do the analysis from the dialogue, situation who delivered in the movie which becomes the main data in this research.
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Manavi, Vallari, Anjali Diwate, Priyanka Korade, and Anita Senathi. "MoView Engine : An Open Source Movie Recommender." ITM Web of Conferences 32 (2020): 03008. http://dx.doi.org/10.1051/itmconf/20203203008.
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Recommendation is an ideology that works as choice-based system for the end users. Users are recommended with their favorite movies based on history of other watched movies or based on the category of the movies. These types of recommendations are becoming popular because of their ability to think and react as human brain. For this purpose, deep learning or artificial intelligence comes into picture. It is the ability to think as a human brain as give the output best suited to the end users liking. This paper focuses on implementing the recommendation system of movies using deep learning with neural network model using the activation function of SoftMax to give an experience to users as friendly recommendation. Moreover, this paper focuses on different scenarios of recommendation like the recommendation based on history, genre of the movie etc.
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Alqahtani, Samah Abdullah, and Munassir Alhamami. "The effect of movies on listening comprehension for Saudi EFL students." Englisia: Journal of Language, Education, and Humanities 11, no. 2 (April 30, 2024): 199. http://dx.doi.org/10.22373/ej.v11i2.20302.
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This study investigated the effectiveness of using movies to improve the listening skills of Saudi EFL students and their attitudes towards movie-based activities. A questionnaire was administered to 147 participants, 113 of whom were female and 34 of whom were male. The participants were divided into four age groups: 18-20 years, 21-25 years, 26-30 years, and above 30 years. Participants were required to have no history of hearing impairment or learning disabilities that may affect listening skills. Descriptive and inferential statistics were used to analyze the data. The results showed that movies had a positive impact on students' listening skills, and most participants had a positive attitude towards using movies as a learning tool. However, some challenges were also identified, such as finding appropriate movies and lacking guidance on effective movie use. The findings of this study have implications for English language teachers, providing insights into the benefits and challenges of using movies, and suggesting the need for training programs on incorporating movies into language teaching. Overall, this study contributes to understanding how technology-based tools like movies can enhance language learning outcomes.
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Rifaldy, Rakhmat, and Erwin Budi Setiawan. "Recommender System Movie Netflix using Collaborative Filtering with Weighted Slope One Algorithm in Twitter." Building of Informatics, Technology and Science (BITS) 4, no. 2 (September 21, 2022): 500–506. http://dx.doi.org/10.47065/bits.v4i2.1959.
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Movies are entertainment that many people enjoy filling their spare time. After watching a movie, people usually write reviews about the movie on social media such as Twitter. As the number of movies grows, a recommendation system is created, which is useful for finding movies they might like based on the movies they have seen. This study developed a movie recommendation system using Collaborative Filtering (CF) with the Weighted Slope One (WSO) algorithm. The dataset used is taken from tweet data on Twitter. Then the tweet dataset is converted into a rating value which will later be used in the recommendation system. This study uses Mean Absolute Error (MAE) to measure accuracy. In Collaborative Filtering, the system gets the best MAE of 0.924. Then for Weighted Slope One, the system gets the best MAE of 0.568.
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MD Rokibul Hasan and Janatul Ferdous. "Dominance of AI and Machine Learning Techniques in Hybrid Movie Recommendation System Applying Text-to-number Conversion and Cosine Similarity Approaches." Journal of Computer Science and Technology Studies 6, no. 1 (January 16, 2024): 94–102. http://dx.doi.org/10.32996/jcsts.2024.6.1.10.
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This research explored movie recommendation systems based on predicting top-rated and suitable movies for users. This research proposed a hybrid movie recommendation system that integrates both text-to-number conversion and cosine similarity approaches to predict the most top-rated and desired movies for the targeted users. The proposed movie recommendation employed the Alternating Least Squares (ALS) algorithm to reinforce the accuracy of movie recommendations. The performance analysis and evaluation were undertaken by employing the widely used "TMDB 5000 Movie Dataset" from the Kaggle dataset. Two experiments were conducted, categorizing the dataset into distinct modules, and the outcomes were contrasted with state-of-the-art models. The first experiment attained a Root Mean Squared Error (RMSE) of 0.97613, while the second experiment expanded predictions to 4800 movies, culminating in a substantially minimized RMSE of 0.8951, portraying a 97% accuracy enhancement. The findings underscore the essence of parameter selection in text-to-number conversion and cosine and the gap for other systems to maintain user preferences for comprehensive and precise data gathering. Overall, the proposed hybrid movie recommendation system demonstrated promising results in predicting top-rated movies and offering personalized and accurate recommendations to users.
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Pitts L, Donna. "Sound levels in movie theaters: is there a potential for hearing loss?" Journal of Otolaryngology-ENT Research 11, no. 2 (2019): 119–22. http://dx.doi.org/10.15406/joentr.2019.11.00420.
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Purpose: The goal of this study was to determine the frequency of attendance at movie theaters, the most popular genres of movies, the sound levels inside movie theaters, and if, based on frequency of attendance, a relationship could be established between temporary threshold shift and noise levels inside movie theaters. Method: A survey was first distributed to moviegoers at several different venues. Movies were selected based on the most popular genres. A noise logging dosimeter was utilized during the viewing of 16 movies in two different multiplex theaters to determine if sound levels exceed those deemed hazardous by the Occupational Safety and Health Administration. Results: Results indicated that most surveyants go to the movie theater about once a week. The noise levels obtained for 16 movies did not exceed those established by OSHA as hazardous in nature. Conclusion: Given that the maximum dose recorded by any movie viewed was 7.6% (out of 100%), it is highly unlikely that the average person would sustain a temporary threshold shift from movie viewing alone. Even if a person attended the movie theater on a daily basis, there is no evidence to suggest that movie viewing alone could cause a temporary shift in hearing.
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R.A., Dr Burange. "Development of Movie Recommendation System Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 1, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29879.
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The past ten years have seen a massive influx of data, which has both improved our lives in certain ways and created a paradox of choice in others. It can be very overwhelming to have so many options for everything from what one eats to what to watch. There is a vast amount of data available in the field of media content, especially movies. This abundance is greater than the options available to previous generations. It is challenging for people to locate content that suits their tastes due to the continuously expanding archive of films. By making recommendations for movies based on user interactions or movie attributes, movie recommendation systems address this problem. This paper looks into the development of a movie recommendation system through the use of machine learning techniques in a content-based manner. The system looks at movie attributes like plot keywords, director, genre, and actors to find movies that are comparable to ones a user has already seen and enjoyed. The primary objective is to enhance the user experience by providing tailored recommendations that are based on the essential elements of movies. A content-based approach is used to identify unique features of each movie and recommend similar options to users with similar preferences. We use algorithms such as Text Vectorization and Cosine Similarity to recommend five similar movies based on a user's previous viewing experience. Key Words: Movie Recommendation System, Recommendation Systems, Content Based, Machine Learning, Cosine Similarity
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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 (February 28, 2021): 54–62. http://dx.doi.org/10.22266/ijies2021.0228.06.
Full textAbstract:
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 are used to evaluate the performance of the proposed HANN method. The hierarchical structures of the twitter data is considered by the proposed HANN method and the most important words related to genre classification is identified by the attention mechanism, where the other neural networks such as Artificial Neural Network and Convolutional Neural Network (CNN) returns only the important weights resulting from previous words. The HANN method has the advantages of encoding the relevant information that helps to improve the performance of the recommendation system. The experimental results show that the HANN method achieve higher performance compared to other classifiers Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). The HANN method achieves accuracy of 73.15% in classification, while the existing BiLSTM method achieve the accuracy of 68% in classification.