Relevant bibliographies by topics / Movie data

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Movie data'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Movie data"

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D.A., Olubukola, Stephen O.M., Funmilayo A.K., Ayokunle O., Oyebola A., Oduroye A., Wumi A., and Yaw M. "Movie Success Prediction Using Data Mining." British Journal of Computer, Networking and Information Technology 4, no. 2 (September 22, 2021): 22–30. http://dx.doi.org/10.52589/bjcnit-cqocirec.

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The movie industry is arguably one of the biggest entertainment sectors. Nollywood, the Nigerian movie industry produces tons of movies for public consumption, but only a few make it to box-office or end up becoming blockbusters. The introduction of movie success prediction can play an important role in the industry not only to predict movie success but to help directors and producers make better decisions for the purpose of profit. This study proposes a movie prediction model that applies data mining techniques and machine learning algorithms to predict the success or failure of an upcoming movie (based on predefined parameters). The parameters needed for predicting the success or failure of a movie include dataset needed for the process of data mining such as the historical data of actors, actresses, writers, directors, marketing and production budget, audience, location, release date, and competing movies on same release date. This model also helps movie consumers to determine a blockbuster, hit, success rating and quality of upcoming movies before deciding on a movie ticket. The data mining techniques was applied to Internet Movie Database MetaData which was initially passed through cleaning and integration process.
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Komurlekar, Runali. "Movie Recommendation Model from Data through Online Streaming." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1549–51. http://dx.doi.org/10.22214/ijraset.2021.37495.

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Abstract: With the Pandemic era and easy availability of internet, potential of digital movie and tv series industry is in huge demand. Hence it has led to developing an automatic movie recommendation engine and has become a popular issue. Some of these problems can be solved or at least be minimized if we take the right decisions on what kind of movies to ignore, what movies to consider. This paper examines the recommendations that are obtained with considering the sample movies that have never got an above-average rating, where average rating is defined here as the mid-value between 0 and maximum rating used, for example, 2.5 in 1 to 5 rating scale. The technique used is “collaborative filtering”. Comparison of different pre-training model, it is tried to maximize the effectiveness of semantic understanding and make the recommendation be able to reflect meticulous perception on the relationship between user utilisation and user preference. Keywords: movie recommendation system, user similarity, user similarity, consumption pattern
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Suzuki, Shigeru. "Automated Movie Production of CloudSat Data." Journal of the Institute of Image Information and Television Engineers 69, no. 2 (2015): 151–54. http://dx.doi.org/10.3169/itej.69.151.

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Hu, Ya-Han, Wen-Ming Shiau, Sheng-Pao Shih, and Cho-Ju Chen. "Considering online consumer reviews to predict movie box-office performance between the years 2009 and 2014 in the US." Electronic Library 36, no. 6 (December 10, 2018): 1010–26. http://dx.doi.org/10.1108/el-02-2018-0040.

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Purpose The purpose of this paper is to combine basic movie information factors, external factors and review factors, to predict box-office performance and identify the most crucial factor of influence for box-office performance. Design/methodology/approach Five movie genres and first-week movie reviews found on IMDb were collected. The movie reviews were quantified using sentiment analysis tools SentiStrength and Stanford CoreNLP, in which quantified data were combined with basic movie information and external environment factors to predict movie box-office performance. A movie box-office performance prediction model was then developed using data mining (DM) technologies with M5 model trees (M5P), linear regression (LR) and support vector regression (SVR), after which movie box-office performance predictions were made. Findings The results of this paper showed that the inclusion of movie reviews generated more accurate prediction results. Concerning movie review-related factors, the one that exhibited the greatest effect on box-office performance was the number of movie reviews made, whereas movie review content only displayed an effect on box-office performance for specific movie genres. Research limitations/implications Because this paper collected movie data from the IMDb, the data were limited and primarily consisted of movies released in the USA; data pertaining to less popular movies or those released outside of the USA were, thus, insufficient. Practical implications This paper helps to verify whether the consideration of the features extracted from movie reviews can improve the performance of movie box-office. Originality/value Through various DM technologies, this paper shows that movie reviews enhanced the accuracy of box-office performance predictions and the content of movie reviews has an effect on box-office performance.
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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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Wang, Yibo, Mingming Wang, and Wei Xu. "A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/8263704.

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Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.
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Huang, Yi-Ting, and Ping-Feng Pai. "Using the Least Squares Support Vector Regression to Forecast Movie Sales with Data from Twitter and Movie Databases." Symmetry 12, no. 4 (April 15, 2020): 625. http://dx.doi.org/10.3390/sym12040625.

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Due to the rapid prominence and popularity of social media, social broadcasting networks with voluntary information sharing have become one of the most powerful ways to spread word-of-mouth opinions, and thus, have influence on consumers’ preferences toward products. Therefore, sentiment analysis data from social media have become more important in forecasting product sales. For the movie industry, the opinions expressed on social media have increasing impacts on movie sales. In addition, some databases, such as the Box Office Mojo and Internet Movie Database (IMDb), contain structured data for predicting movie sales. Thus, three categories of data—data of movie databases, data of tweets, and hybrid data including movies databases and tweets—are employed symmetrically in this study. The aim of this study is to employ the least squares support vector regression (LSSVR) to forecast movie sales worldwide according to these three forms of data. In addition, three other forecasting techniques—namely, the back propagation neural network (BPNN), the generalized regression neural network (GRNN), and the multivariate linear regression (MLR) model—were used to forecast movie sales with the three types of data. The empirical results show that the LSSVR model with hybrid data can obtain more accurate results than the other forecasting models with all data types. Thus, forecasting movie sales using the LSSSVR model with data containing movie databases and tweets is a feasible and prospective method to forecast movie sales.
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Jagjeet Singh and Vibhor Sharma. "Movie Genre Prediction Based on Plot Synopsis." November 2020 6, no. 11 (November 23, 2020): 118–21. http://dx.doi.org/10.46501/ijmtst061121.

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Movies have now become one of the main sources of entertainment for people. The extensive use of Internet has increased the creation and sharing of movie related data online. Movie plot summaries generally tell about the movie genres and many people read them before deciding to watch a movie. An automatic system can be applied to predict genres based on summaries. The objective dataset chosen by us consists of 14828 movies taken from Kaggle. We use different approaches such as TFIDF, Char gram, Skip gram etc to get better accuracy scores in predicting movie genre tags.
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V R, Nithin. "Predicting Movie Success Based On Imdb Data." International Journal for Research in Applied Science and Engineering Technology V, no. X (October 22, 2017): 504–7. http://dx.doi.org/10.22214/ijraset.2017.10074.

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Nithin, VR, M. Pranav, PB Sarath Babu, and A. Lijiya. "Predicting Movie Success Based on IMDB Data." International Journal of Business Intelligents 003, no. 002 (December 15, 2014): 34–36. http://dx.doi.org/10.20894/ijbi.105.003.002.004.

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Dissertations / Theses on the topic "Movie data"

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Müglich, Marcel. "Motion Feature Extraction of Video and Movie Data." Thesis, KTH, Numerisk analys, NA, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214030.

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Since the Video on Demand market grows at a fast rate in terms of available content and user numbers, the task arises to match personal relevant content to each individual user. This problem is tackled by implementing a recommondation system which finds relevant content by automatically detecting patterns in the individual user’s behaviour. To find such patterns, either collaborative filtering, which evaluates patterns of user groups to draw conclusions about a single user’s preferences, or content based strategies can be applied. Those content strategies analyze the watched movies of the individual user and extract quantifiable information from them. This information can be utilized to find relevant movies with similar features. The focus of this thesis lies on the extraction of motion features from movie and video data. Three feature extraction methods are presented and evaluated which classify camera movement, estimate the motion intensity and detect film transitions.
VOD-marknaden (Video på begäran) är en växande marknad, dels i mängden tillgängligt innehåll samt till antalet användare. Det skapar en utmaning att matcha personligt relevant innehåll för varje enskild användare. Utmaningen hanteras genom att implementera ett rekommendationssystem som hittar relevant innehåll genom att automatiskt identifiera mönster i varje användaren beteende. För att hitta sådana mönster används i vanliga fall Collaborative filtering; som utvärderar mönster utifrån grupper av flera användare och kors- rekommenderar produkter mellan dem utan att ta nämnvärd hänsyn till produktens innehåll. (De som har köpt X har också köpt Y) Ett alternativ till detta är att tillämpa en innehållsbaserad strategi. Innehållsbaserade strategier analyserar den faktiska video-datan i de produkter som har konsumerats av en enskild användare med syfte att därifrån extrahera kvantifierbar information. Denna information kan användas för att hitta relevanta filmer med liknande videoinnehåll. Inriktningen för denna avhandling berör utvinning av kamerarörelsevektorer från film- och videodata. Tre extraktionsmetoder presenteras och utvärderas för att klassificera kamerans rörelse, kamerarörelsen intensitet och för att detektera scenbyten.
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Almadi, Kanika. "Quantitative study of the movie industry based on IMDb data." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113502.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 47).
Big Data Analytics is an emerging business capability that is providing far more intelligence to the companies nowadays to make well-informed decisions and better formulate their business strategies. This has been made possible due to easy accessibility of immense volume of data stored in clouds in a secure manner. As a result, online product review platforms have also gained enormous popularity and are successfully providing various services to the consumers primarily via user-generated content. The thesis makes use of raw and unstructured data available on IMDB website, cleans it up and organizes it in a structured format suitable for quick analysis by various analytical softwares. The thesis then examines the available literature on analytics done on IMDB movie dataset and identifies that little work has been carried out in predicting the financial success of the movies. The thesis thus carries out data analytics on the IMDB movie sets and highlights several parameters like movie interconnectedness and director's credentials, which correlates positively with the movie gross revenue. The thesis thereafter loosely defines a movie innovative index encompassing of parameters like number of references, number of follows and number of remake and discusses how the abundance of some of these parameters have a positive impact on box office success of the movie. Contrarily the lack of presence of these parameters thereby characterizing an innovative movie may not be so well received by the audiences thus leading to poor box office performance. The thesis also proposes how the director's credentials in the film industry measured by his/her total number of nominations and awards winning in the Oscar have a positive impact on the financial success of the movie and their own career advancement.
by Kanika Almadi.
S.M. in Engineering and Management
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Wrenn, Alex. "Differences in Seasonality Based on Movie Quality." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2029.

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In recent years, the entertainment industry has begun to announce the release dates of many of their movies years in advance. This leads one to believe that movie studios are not taking into account the quality of a movie when a studio decides its release date. This paper will be an analysis in whether there is a difference in seasonality between different qualities of movies. If a studio announces the release date before filming even begins, it is clear that they do not know, and therefore cannot properly consider, the quality of the movie when they make its release date public. I will use films that make over a million dollars at the box office from 2000-2016 to examine the seasonality of good, average, and bad movies. My models will control for variables that were found to be significant in previous research. These include budget, MPAA rating, genre, and Oscar nominations. I will prove that there is a difference in seasonality between all three of these qualities groups. This will show that the Hollywood is now dismissing a key component in the difficult decision process that is movie release dates.
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Arroniz, Inigo. "EXTRACTING QUANTITATIVE INFORMATIONFROM NONNUMERIC MARKETING DATA: AN AUGMENTEDLATENT SEMANTIC ANALYSIS APPROACH." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3083.

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Despite the widespread availability and importance of nonnumeric data, marketers do not have the tools to extract information from large amounts of nonnumeric data. This dissertation attempts to fill this void: I developed a scalable methodology that is capable of extracting information from extremely large volumes of nonnumeric data. The proposed methodology integrates concepts from information retrieval and content analysis to analyze textual information. This approach avoids a pervasive difficulty of traditional content analysis, namely the classification of terms into predetermined categories, by creating a linear composite of all terms in the document and, then, weighting the terms according to their inferred meaning. In the proposed approach, meaning is inferred by the collocation of the term across all the texts in the corpus. It is assumed that there is a lower dimensional space of concepts that underlies word usage. The semantics of each word are inferred by identifying its various contexts in a document and across documents (i.e., in the corpus). After the semantic similarity space is inferred from the corpus, the words in each document are weighted to obtain their representation on the lower dimensional semantic similarity space, effectively mapping the terms to the concept space and ultimately creating a score that measures the concept of interest. I propose an empirical application of the outlined methodology. For this empirical illustration, I revisit an important marketing problem, the effect of movie critics on the performance of the movies. In the extant literature, researchers have used an overall numerical rating of the review to capture the content of the movie reviews. I contend that valuable information present in the textual materials remains uncovered. I use the proposed methodology to extract this information from the nonnumeric text contained in a movie review. The proposed setting is particularly attractive to validate the methodology because the setting allows for a simple test of the text-derived metrics by comparing them to the numeric ratings provided by the reviewers. I empirically show the application of this methodology and traditional computer-aided content analytic methods to study an important marketing topic, the effect of movie critics on movie performance. In the empirical application of the proposed methodology, I use two datasets that combined contain more than 9,000 movie reviews nested in more than 250 movies. I am restudying this marketing problem in the light of directly obtaining information from the reviews instead of following the usual practice of using an overall rating or a classification of the review as either positive or negative. I find that the addition of direct content and structure of the review adds a significant amount of exploratory power as a determinant of movie performance, even in the presence of actual reviewer overall ratings (stars) and other controls. This effect is robust across distinct opertaionalizations of both the review content and the movie performance metrics. In fact, my findings suggest that as we move from sales to profitability to financial return measures, the role of the content of the review, and therefore the critic's role, becomes increasingly important.
Ph.D.
Department of Marketing
Business Administration
Business Administration PhD
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Wu, Yuk Ying. "Movie allocation in parallel video servers /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20WU.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 69-76). Also available in electronic version. Access restricted to campus users.
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Karaman, Hilal. "A Content Based Movie Recommendation System Empowered By Collaborative Missing Data Prediction." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612037/index.pdf.

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The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question &ldquo
Which one should I choose?&rdquo
arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including content-based and collaborative techniques which are the most commonly used approaches in recommendation systems. This thesis work introduces ReMovender, a content-based movie recommendation system which is empowered by collaborative missing data prediction. The distinctive point of this study lies in the methodology used to correlate the users in the system with one another and the usage of the content information of movies. ReMovender makes it possible for the users to rate movies in a scale from one to five. By using these ratings, it finds similarities among the users in a collaborative manner to predict the missing ratings data. As for the content-based part, a set of movie features are used in order to correlate the movies and produce recommendations for the users.
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Ma, Ke. "Content-based Recommender System for Movie Website." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188494.

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Recommender System is a tool helping users find content and overcome information overload. It predicts interests of users and makes recommendation according to the interest model of users. The original content-based recommender system is the continuation and development of collaborative filtering, which doesn’t need the user’s evaluation for items. Instead, the similarity is calculated based on the information of items that are chose by users, and then make the recommendation accordingly. With the improvement of machine learning, current content-based recommender system can build profile for users and products respectively. Building or updating the profile according to the analysis of items that are bought or visited by users. The system can compare the user and the profile of items and then recommend the most similar products. So this recommender method that compare user and product directly cannot be brought into collaborative filtering model. The foundation of content-based algorithm is acquisition and quantitative analysis of the content. As the research of acquisition and filtering of text information are mature, many current content-based recommender systems make recommendation according to the analysis of text information. This paper introduces content-based recommender system for the movie website of VionLabs. There are a lot of features extracted from the movie, they are diversity and unique, which is also the difference from other recommender systems. We use these features to construct movie model and calculate similarity. We introduce a new approach for setting weight of features, which improves the representative of movies. Finally we evaluate the approach to illustrate the improvement.
Recommender System är ett verktyg som hjälper användarna att hitta innehåll och övervinna informationsöverflöd. Det förutspår användarnas intressen och gör rekommendation enligt räntemodellen användare. Den ursprungliga innehållsbaserade recommender är en fortsättning och utveckling av samarbete filtrering, som inte behöver användarens utvärdering artiklar. Istället är likheten beräknas baserat på informationen objekt som har varit valde av användare, och sedan göra rekommendationen därefter. Med förbättringen av maskininlärning, kan nuvarande innehållsbaserad recommender systemet bygga profil för användare och produkt respektive. Bygga eller uppdatera profilen enligt analysen av objekt som köps eller besöks av användare. Systemet kan jämföra användaren och profilen av artiklar och rekommendera den mest liknande produkt. Så här recommender metod som jämför användaren och produkten direkt kan inte föras in collaborative filtreringsmodell. Grunden för innehållsbaserad algoritm är förvärv och kvantitativ analys av innehållet. Eftersom forskning förvärv och filtrering av textinformation är mogen, många aktuella innehållsbaserade recommender system gör rekommendation enligt analysen av textinformation. Denna uppsats införa innehållsbaserad recommender system för film webbplats VionLabs. Det finns en mängd funktioner som extraherats från en film, är de mångfald och unik, vilket är också skillnaden med andra recommender system. Vi använder dessa funktioner för att konstruera film vektor och beräkna likheter. Vi introducerar en ny metod för att fastställa vikten av funktioner, vilket förbättrar företrädare för filmer. Slutligen utvärderar vi tillvägagångssättet för att illustrera förbättringen.
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Peng, Fengjiao S. M. Massachusetts Institute of Technology. "My Personalized Movies : novel system for automatically animating a movie based on personal data and evaluation of its impact on affective and cognitive experience." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120674.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 73-77).
Storytelling is a fundamental way in which human beings make sense of the world. Animated movies tell stories that engage audience across culture and age groups. I designed and built My Personalized Movies (MPM), a novel system where animated stories are automatically created based on data provided by individuals. The data include self-tracked mood and behavior captured in quantitative measures and descriptive text. MPM is designed to engage viewers through an emotive narrative, induce self-reflection about their mood and behavior patterns, and to improve self-compassion and self-esteem, which mediates behavior change. I demonstrate with a few stages of studies, involving in total 107 participants, that viewers show strong emotional engagement with MPM and can explicitly connect animated characters' stories to one's past experiences. An analysis of 22 participants' facial expression data during MPM reveals that participants' change in implicit self-esteem is positively correlated with the happiness of their facial expression. Participants with higher depression severity, as measured by PHQ9, showed less positive facial expression at the happy moments in the animation.
by Fengjiao Peng.
S.M.
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El, Aouad Sara. "Personalized, Aspect-based Summarization of Movie Reviews." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS019.pdf.

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Les sites web de critiques en ligne aident les utilisateurs à décider quoi acheter ou quels hôtels choisir. Ces plateformes permettent aux utilisateurs d’exprimer leurs opinions à l’aide d’évaluations numériques et de commentaires textuels. Les notes numériques donnent une idée approximative du service. D'autre part, les commentaires textuels donnent des détails complets, ce qui est fastidieux à lire. Dans cette thèse, nous développons de nouvelles méthodes et algorithmes pour générer des résumés personnalisés de critiques de films, basés sur les aspects, pour un utilisateur donné. Le premier problème que nous abordons consiste à extraire un ensemble de mots liés à un aspect des critiques de films. Notre évaluation montre que notre méthode est capable d'extraire même des termes impopulaires qui représentent un aspect, tels que des termes composés ou des abréviations. Nous étudions ensuite le problème de l'annotation des phrases avec des aspects et proposons une nouvelle méthode qui annote les phrases en se basant sur une similitude entre la signature d'aspect et les termes de la phrase. Le troisième problème que nous abordons est la génération de résumés personnalisés, basés sur les aspects. Nous proposons un algorithme d'optimisation pour maximiser la couverture des aspects qui intéressent l'utilisateur et la représentativité des phrases dans le résumé sous réserve de contraintes de longueur et de similarité. Enfin, nous réalisons trois études d’utilisateur qui montrent que l’approche que nous proposons est plus performante que la méthode de pointe en matière de génération de résumés
Online reviewing websites help users decide what to buy or places to go. These platforms allow users to express their opinions using numerical ratings as well as textual comments. The numerical ratings give a coarse idea of the service. On the other hand, textual comments give full details which is tedious for users to read. In this dissertation, we develop novel methods and algorithms to generate personalized, aspect-based summaries of movie reviews for a given user. The first problem we tackle is extracting a set of related words to an aspect from movie reviews. Our evaluation shows that our method is able to extract even unpopular terms that represent an aspect, such as compound terms or abbreviations, as opposed to the methods from the related work. We then study the problem of annotating sentences with aspects, and propose a new method that annotates sentences based on a similarity between the aspect signature and the terms in the sentence. The third problem we tackle is the generation of personalized, aspect-based summaries. We propose an optimization algorithm to maximize the coverage of the aspects the user is interested in and the representativeness of sentences in the summary subject to a length and similarity constraints. Finally, we perform three user studies that show that the approach we propose outperforms the state of art method for generating summaries
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Persson, Karl. "Predicting movie ratings : A comparative study on random forests and support vector machines." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11119.

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The aim of this work is to evaluate the prediction performance of random forests in comparison to support vector machines, for predicting the numerical user ratings of a movie using pre-release attributes such as its cast, directors, budget and movie genres. In order to answer this question an experiment was conducted on predicting the overall user rating of 3376 hollywood movies, using data from the well established movie database IMDb. The prediction performance of the two algorithms was assessed and compared over three commonly used performance and error metrics, as well as evaluated by the means of significance testing in order to further investigate whether or not any significant differences could be identified. The results indicate some differences between the two algorithms, with consistently better performance from random forests in comparison to support vector machines over all of the performance metrics, as well as significantly better results for two out of three metrics. Although a slight difference has been indicated by the results one should also note that both algorithms show great similarities in terms of their prediction performance, making it hard to draw any general conclusions on which algorithm yield the most accurate movie predictions.
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Books on the topic "Movie data"

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Beattie, Rob. Digital movie making. New York: DK Pub., 2002.

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Underdahl, Keith. Microsoft Windows Movie Maker for dummies. Foster City, CA: IDG Books Worldwide, 2000.

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Underdahl, Keith. Microsoft Windows Movie Maker for dummies. Foster City, CA: IDG Books Worldwide, 2000.

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Microsoft Windows Movie Maker 2. Berkeley, CA: Peachpit Press, 2004.

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Mullen, Tony. Blender studio projects: Digital movie-making. Indianapolis: Wiley Technology Pub., 2010.

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1970-, Andaur Claudio, ed. Blender studio projects: Digital movie-making. Indianapolis, Ind: Wiley Pub., 2010.

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Carlson, Jeff. Making a movie in iMovie and iDVD. Berkeley, CA: Peachpit Press, 2005.

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Li, Ying. Video Content Analysis Using Multimodal Information: For Movie Content Extraction, Indexing and Representation. Boston, MA: Springer US, 2003.

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Associates, Paul Kagan. Kagan's The PPV household: A detailed analysis of PPV movie and event buying patterns based on actual field data. Carmel, CA: Paul Kagan Associates, 2001.

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Ludington, Jake. Easy digital home movies. Indianapolis, Ind: Que, 2004.

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Book chapters on the topic "Movie data"

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Haughton, Dominique, Mark-David McLaughlin, Kevin Mentzer, and Changan Zhang. "What Does “Big Data” Mean? The Data Scientist Point of View." In Movie Analytics, 3–4. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09426-7_2.

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Haughton, Dominique, Mark-David McLaughlin, Kevin Mentzer, and Changan Zhang. "What Do We Know About Analyzing Movie Data?" In Movie Analytics, 1–2. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09426-7_1.

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Haughton, Dominique, Mark-David McLaughlin, Kevin Mentzer, and Changan Zhang. "Can We Predict Oscars from Twitter and Movie Review Data?" In Movie Analytics, 41–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09426-7_6.

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Kharb, Latika, Deepak Chahal, and Vagisha. "Forecasting Movie Rating Through Data Analytics." In Data Science and Analytics, 249–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5830-6_21.

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Ma, Manqing, Wei Pang, Lan Huang, and Zhe Wang. "A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks." In Advances in Knowledge Discovery and Data Mining, 750–61. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_58.

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Saraswat, Mala, and Shampa Chakraverty. "Leveraging Movie Recommendation Using Fuzzy Emotion Features." In Data Science and Analytics, 475–83. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8527-7_40.

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Ostuni, Vito Claudio, Giosia Gentile, Tommaso Di Noia, Roberto Mirizzi, Davide Romito, and Eugenio Di Sciascio. "Mobile Movie Recommendations with Linked Data." In Availability, Reliability, and Security in Information Systems and HCI, 400–415. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40511-2_29.

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Lakshmi Devi, B., V. Varaswathi Bai, Somula Ramasubbareddy, and K. Govinda. "Sentiment Analysis on Movie Reviews." In Emerging Research in Data Engineering Systems and Computer Communications, 321–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0135-7_31.

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Doshi, Pratik, and Wlodek Zadrozny. "Movie Genre Detection Using Topological Data Analysis." In Statistical Language and Speech Processing, 117–28. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00810-9_11.

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Lee, Maria R., Tsung Teng Chen, and Ying Shun Cai. "Amalgamating Social Media Data and Movie Recommendation." In Knowledge Management and Acquisition for Intelligent Systems, 141–52. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42706-5_11.

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Conference papers on the topic "Movie data"

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Nie, Dong, Lingzi Hong, and Tingshao Zhu. "Movie Recommendation Using Unrated Data." In 2013 12th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2013. http://dx.doi.org/10.1109/icmla.2013.70.

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Roy, Debashish, and Chen Ding. "Movie Recommendation using YouTube Movie Trailer Data as the Side Information." In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2020. http://dx.doi.org/10.1109/asonam49781.2020.9381349.

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Tice, Bradley S. "Compressed data for the movie industry." In SPIE OPTO, edited by Guifang Li. SPIE, 2013. http://dx.doi.org/10.1117/12.2035187.

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Ahmad, Javaria, Prakash Duraisamy, Amr Yousef, and Bill Buckles. "Movie success prediction using data mining." In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2017. http://dx.doi.org/10.1109/icccnt.2017.8204173.

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Li, Min, Chunfang Li, and Minyong Shi. "Movie Data Visualization Based on WebGL." In 2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter). IEEE, 2021. http://dx.doi.org/10.1109/snpdwinter52325.2021.00023.

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Liu, Ye, Jiawei Zhang, Chenwei Zhang, and Philip S. Yu. "Data-driven Blockbuster Planning on Online Movie Knowledge Library." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622316.

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Xie, Yuxiang, Xidao Luan, Jingmeng He, Lili Zhang, Xin Zhang, and Chen Li. "A Movie Summary Generation System." In 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC). IEEE, 2017. http://dx.doi.org/10.1109/dsc.2017.96.

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Sinha, Ankit A., S. V. Vamsi Krishna, Rajashree Shedge, and Avi Sinha. "Movie production investment decision system." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8390215.

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Li, Xiaoyue, Haonan Zhao, Zhuo Wang, and Zhezhou Yu. "Research on Movie Rating Prediction Algorithms." In 2020 5th IEEE International Conference on Big Data Analytics (ICBDA). IEEE, 2020. http://dx.doi.org/10.1109/icbda49040.2020.9101282.

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YuMin, Su, Zhang Yuan, and Yan JinYao. "Neural Network Based Movie Rating Prediction." In ICBDC '18: 2018 International Conference on Big Data and Computing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3220199.3220204.

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Reports on the topic "Movie data"

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Robertson, D. W. Use of a distributed movie-making system for presentation of fluid flow data. Office of Scientific and Technical Information (OSTI), May 1988. http://dx.doi.org/10.2172/5921002.

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Rogers, G. D., and B. K. Kerans. A system of computer programs (WAT{_}MOVE) for transferring data among data bases in the US Geological Survey National Water Information System. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/138226.

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Peck, H., and I. Gomez. Imaging Materials Using Movie Mode Dynamic Transmission Electron Microscopes Final Report CRADA No. TC02184.0 Date Technical Work Ended: February 1st, 2014. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1771027.

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Gowdy, M. J., M. P. Smits, P. L. Wilkey, and S. F. Miller. Data summary report on short-term turbidity monitoring of pipeline river crossings in the Moyie River, Boundary County, Idaho: PGT-PG&E Pipeline Expansion Project. Office of Scientific and Technical Information (OSTI), March 1994. http://dx.doi.org/10.2172/10161518.

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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
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Coulson, Saskia, Melanie Woods, Drew Hemment, and Michelle Scott. Report and Assessment of Impact and Policy Outcomes Using Community Level Indicators: H2020 Making Sense Report. University of Dundee, 2017. http://dx.doi.org/10.20933/100001192.

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Making Sense is a European Commission H2020 funded project which aims at supporting participatory sensing initiatives that address environmental challenges in areas such as noise and air pollution. The development of Making Sense was informed by previous research on a crowdfunded open source platform for environmental sensing, SmartCitizen.me, developed at the Fab Lab Barcelona. Insights from this research identified several deterrents for a wider uptake of participatory sensing initiatives due to social and technical matters. For example, the participants struggled with the lack of social interactions, a lack of consensus and shared purpose amongst the group, and a limited understanding of the relevance the data had in their daily lives (Balestrini et al., 2014; Balestrini et al., 2015). As such, Making Sense seeks to explore if open source hardware, open source software and and open design can be used to enhance data literacy and maker practices in participatory sensing. Further to this, Making Sense tests methodologies aimed at empowering individuals and communities through developing a greater understanding of their environments and by supporting a culture of grassroot initiatives for action and change. To do this, Making Sense identified a need to underpin sensing with community building activities and develop strategies to inform and enable those participating in data collection with appropriate tools and skills. As Fetterman, Kaftarian and Wanderman (1996) state, citizens are empowered when they understand evaluation and connect it in a way that it has relevance to their lives. Therefore, this report examines the role that these activities have in participatory sensing. Specifically, we discuss the opportunities and challenges in using the concept of Community Level Indicators (CLIs), which are measurable and objective sources of information gathered to complement sensor data. We describe how CLIs are used to develop a more indepth understanding of the environmental problem at hand, and to record, monitor and evaluate the progress of change during initiatives. We propose that CLIs provide one way to move participatory sensing beyond a primarily technological practice and towards a social and environmental practice. This is achieved through an increased focus in the participants’ interests and concerns, and with an emphasis on collective problem solving and action. We position our claims against the following four challenge areas in participatory sensing: 1) generating and communicating information and understanding (c.f. Loreto, 2017), 2) analysing and finding relevance in data (c.f. Becker et al., 2013), 3) building community around participatory sensing (c.f. Fraser et al., 2005), and 4) achieving or monitoring change and impact (c.f. Cheadle et al., 2000). We discuss how the use of CLIs can tend to these challenges. Furthermore, we report and assess six ways in which CLIs can address these challenges and thereby support participatory sensing initiatives: i. Accountability ii. Community assessment iii. Short-term evaluation iv. Long-term evaluation v. Policy change vi. Capability The report then returns to the challenge areas and reflects on the learnings and recommendations that are gleaned from three Making Sense case studies. Afterwhich, there is an exposition of approaches and tools developed by Making Sense for the purposes of advancing participatory sensing in this way. Lastly, the authors speak to some of the policy outcomes that have been realised as a result of this research.
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Patterns and implications of male migration for HIV prevention strategies in Karnataka, India. Population Council, 2008. http://dx.doi.org/10.31899/hiv16.1004.

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Karnataka is one of the high HIV prevalence states in India. Results from the National Family Health Survey indicate that 0.69 percent of adults aged 15–49 were infected with HIV in 2005–06. According to sentinel surveillance system data, HIV prevalence among pregnant women receiving antenatal care (ANC) in Karnataka was 1.3 percent. Further, 18 of the state's 27 districts have recorded HIV prevalence of more than 1 percent among pregnant women receiving ANC in sentinel sites. Strong male migration patterns are evident in some of the state’s high HIV prevalence districts. According to the 2001 census, Karnataka ranks fourth in terms of total in-migration, with 2.2 million men on the move from 1991 to 2001. These northern districts are particularly vulnerable to HIV infection. To inform HIV prevention efforts, the Population Council studied patterns and motivations related to migration of male laborers and their links with HIV risk. As part of this study, the Council conducted a systematic analysis of 2001 census data on migration and district-level sentinel surveillance data on HIV prevalence. The purpose of the research was to document patterns of male migration and determine whether there was a relationship between migration and HIV prevalence.
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