Sentiment analysis of movie reviews: finding most important movie aspects using driving factors

The opinion conveyed by the user towards the movie can be understood by sentiment analysis of the movie review. In the current work we focus on finding the aspects of a movie review which direct its polarity the most. This is achieved using certain driving factors, which are scores given to the various movie aspects. Generally its found that aspects with high driving factors affect the review polarity the most.     

Exploiting character networks for movie summarization

Movie summarization focuses on providing as much information as possible for shorter movie clips while still keeping the content of the original movie and presenting a faster way for the audience to understand the movie. In this paper, we propose a novel method to summarize a movie based on character network analysis and the appearance of protagonist and main characters in the movie. Experiments were carried out for 2 movies (Titanic (1997) and Frozen (2013)) to show that our method outperforms conventional approaches in terms of the movie summarization rate.     

Mining movie archives for song sequences

Music and songs are integral parts of Bollywood movies. Every movie of two to three hours, contains three to ten songs, each song is 3–10 min long. Music lovers like to listen music and songs of a movie, however it is time consuming and error prone to search manually all the songs in a movie. Moreover, the task becomes much harder when songs are to be extracted from a huge archived movies’ database containing hundreds of movies. This paper presents an approach to automatically extract music and songs from archived musical movies. We used song grammar to construct Markov Chain Model that differentiates song scenes from dialogue and action scenes in a movie. We tested our system on Bollywood, Hollywood, Pakistani, Bengali, and Tamil movies. A total of 20 movies from different industries were selected for the experiments. On Bollywood movies, we achieved 97.22% recall in song extraction, whereas the recall on Hollywood musical movies is 80%. The test result on Pakistani, Tamil and Bengali movies is 87.09%.     

A hybrid approach for movie recommendation

Collaborative and content-based filtering are the major methods in recommender systems that predict new items that users would find interesting. Each method has advantages and shortcomings of its own and is best applied in specific situations. Hybrid approaches use elements of both methods to improve performance and overcome shortcomings. In this paper, we propose a hybrid approach based on content-based and collaborative filtering, implemented in MoRe, a movie recommendation system. We also provide empirical comparison of the hybrid approach to the base methods of collaborative and content-based filtering and draw useful conclusions upon their performance.     

Interactive movie elements in a pervasive game

This paper presents our research initiatives around new user experiences in the area of pervasive mixed reality crossmedia gaming. In a prototypical game, The Epidemic Menace, we combined static and interactive story telling elements to create a mixed reality experience. As part of this research initiative we also investigated how elements of the real world and virtual worlds could produce a new environment where physical and digital objects can co-exist and interact with each other, we wanted to evaluate how linear stories could be integrated into a game flow and how they could be used to extend game experiences. A new form of interactive entertainment that is not limited to hardware based games or linear storylines but includes a truly interactive TV experience was one of our goals.     

Spark平台下基于上下文信息的影片混合推荐

响应速度较慢和推荐内容与用户上下文信息匹配程度低是当前影片推荐系统迫切需要解决的问题。针对上述挑战,提出Spark平台下基于上下文信息的影片混合推荐方法。它利用分布式并行计算技术Spark进行加速,来提高系统对于海量数据的检索与计算速度,从而减少了系统响应时间。同时该方法将“上下文推荐”和“交替最小二乘的协同过滤（ALS）”融合成一种混合推荐方法,提高了系统的推荐精度。实验结果表明,所提出的混合推荐方法有不错的效果。     

A fusion-based approach to digital movie restoration

Many algorithms have been proposed in literature for digital movie restoration; unfortunately, none of them ensures a perfect result whichever is the image sequence to be restored. Here we propose a new digital scratch restoration algorithm which achieves accuracy results higher than that of already existing algorithms and naturally adapts for implementation into high-performance computing environments.The basic idea of the proposed algorithm is to adopt several relatively well-settled algorithms for the problem at hand and combine obtained results through suitable image fusion techniques, with the aim of taking advantage of the adopted algorithms’ capabilities and, at the same time, limiting their deficiencies.Extensive experiments on real image sequences deeply investigate both accuracy results of the presented scratch restoration approach, which is shown to outperform other existing approaches, and performance of its parallel implementation, which allows for real-time restoration.     

电影本体的构建与一致性分析

针对移动网络对电影服务系统提出的更高要求和电影领域知识描述的不足,阐述构建电影领域本体(MO)的必要性和可行性：首先,概述电影本体的对象和组成,提出构建电影领域本体模型的原则和方法,利用OWL和Protege 4.1构建模型;其次,详细说明电影本体的类、属性、实例、公理和推理规则的具体表述;最后,分析电影本体的一致性,包括类间关系的一致性分析和基于公理的一致性分析。     

Video encryption - the epic movie battle

Security always has to be weighed against cost on the one hand and convenience on the other. But when the stakes are very high this compromise becomes skewed so that costs soar and user convenience plunges. This is the situation now emerging in the world of digital video given the scale of potential losses facing Hollywood and the broadcast industry in the emerging video Internet. The Motion Picture Association of America (MPAA) is determined that digital video content be encrypted at any place where it could potentially be copied, including not just interfaces such as USB ports and PCMCIA slots, but also the internal PCI buses of PCs. This will impose a significant cost because it will require PC makers to develop and integrate components capable of handling the encrypted video while still ensuring that it can be watched by the user. But this is good news for makers of dedicated onboard processors, who were rubbing their hands in glee at the recent Embedded Systems Conference in San Francisco.     

A movie recommendation algorithm based on genre correlations

Since the late 20th century, the number of Internet users has increased dramatically, as has the number of Web searches performed on a daily basis and the amount of information available. A huge amount of new information is transferred to the Web on a daily basis. However, not all data are reliable and valuable, which implies that it may become more and more difficult to obtain satisfactory results from Web searches. We often iterate searches several times to find what we are looking for. To solve this problem, researchers have suggested the use of recommendation systems. Instead of searching for the same information several times, a recommendation system proposes relevant information. In the Web 2.0 era, recommendation systems often rely on collaborative filtering by users. In general, a collaborative filtering approach based on user information such as gender, location, or preference is effective. However, the traditional approach can fail due to the cold-start problem or the sparsity problem, because initial user information is required for this approach to be effective. Recently, several attempts have been made to tackle these collaborative filtering problems. One such attempt used category correlations of contents. For instance, a movie has genre information provided by movie experts and directors. This category information is more reliable than user ratings. Moreover, newly created content always has category information, allowing avoidance of the cold-start problem. In this study, we consider a movie recommendation system and improve the previous algorithms based on genre correlations to correct its shortcomings. We also test the modified algorithm and analyze the results with respect to two characteristics of genre correlations.     

Texture mixing and texture movie synthesis using statisticallearning

We present an algorithm based on statistical learning for synthesizing static and time-varying textures matching the appearance of an input texture. Our algorithm is general and automatic and it works well on various types of textures, including 1D sound textures, 2D texture images, and 3D texture movies. The same method is also used to generate 2D texture mixtures that simultaneously capture the appearance of a number of different input textures. In our approach, input textures are treated as sample signals generated by a stochastic process. We first construct a tree representing a hierarchical multiscale transform of the signal using wavelets. From this tree, new random trees are generated by learning and sampling the conditional probabilities of the paths in the original tree. Transformation of these random trees back into signals results in new random textures. In the case of 2D texture synthesis, our algorithm produces results that are generally as good as or better than those produced by previously described methods in this field. For texture mixtures, our results are better and more general than those produced by earlier methods. For texture movies, we present the first algorithm that is able to automatically generate movie clips of dynamic phenomena such as waterfalls, fire flames, a school of jellyfish, a crowd of people, etc. Our results indicate that the proposed technique is effective and robust     

Embedded learning for computerized production of movie trailers

Movie trailers are usually extracted from the most exciting, interesting, or other noteworthy parts of the movies in order to attract the audience and persuade them to see the film. At present, hand-crafted movie trailers currently occupy almost all the filming market, which is costly and time-consuming. In this paper, we propose an embedded learning algorithm to generate movie trailers automatically without human interventions. Firstly, we use CNN to extract features of candidate frames from the film by a rank-tracing technique. Secondly, SURF algorithm is utilized to match the frames of the movie with the corresponding trailer, thus the labeled and unlabeled dataset are prepared. Thirdly, the mutual information theory is introduced into the embedded machine learning to formulate a new embedded classification algorithm and hence characterize similar key elements of the trailers. Finally, semi-supervised support vector machine is applied as the classifier to obtain the satisfactory key frames to produce the predicted trailers. By treating several famous movies and their manual handling trailers as the ground-truth, series of experiments are carried out, which indicate that our method is feasible and competitive, providing a good potential for promoting the rapid development of the film industry in terms of publicity, as well as providing users with possible solutions for filtering large amounts of Internet videos.     

Semantic movie summarization based on string of IE-RoleNets

Computational Visual Media - Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie...     

Adolescent self-control and music and movie piracy

Recent studies have applied Grottfredson and Hirschi’s General Theory of Crime to investigate whether college students’ inclinations toward internet software piracy is related to low self-control and opportunity (i.e., computer ownership). Given the widespread use of the internet to illegally obtain copies of music and movies, it is important to understand the factors in this form of piracy as well. This study applied the self-control perspective to examine the attitudes of high school students toward the internet piracy of music and movies. Attitudes toward the internet piracy of music and movies were related to self-control, biological sex, internet experience, affiliation with deviant peers, and grade level in this study of high school students. This information is important because studies of internet piracy had not yet confirmed the presence and predictability of internet piracy in high school students. This study also suggests that piracy prevention efforts may be most appropriately focused on high school age individuals and directed toward increasing self-control.     

情感分析在电影推荐系统中的应用

基于用户的协同过滤推荐算法是通过分析用户行为寻找相似用户的集合,其核心是用户兴趣模型的建立以及用户间相似度的计算。传统的用户推荐算法是根据用户评分或者物品信息等行为数据进行个性化推荐,准确率比较低。充分考虑在线评论对于用户之间兴趣相似度的作用,通过对评论的情感分析,构建准确的用户兴趣模型,若用户在评论中表现出来的相似度越高,则表示用户之间的兴趣越相似。实验表明,和传统的基于用户的协同过滤推荐算法相比,基于评论情感分析的协同过滤推荐算法,无论准确率还是召回率都有明显提高。     

Predicting movie box-office revenues using deep neural networks

Neural Computing and Applications - In the film industry, the ability to predict a movie’s box-office revenues before its theatrical release can decrease its financial risk. However, accurate...     

Argument-based mixed recommenders and their application to movie suggestion

Recommender systems have become prevalent in recent years as they help users to access relevant items from the vast universe of possibilities available these days. Most existing research in this area is based purely on quantitative aspects such as indices of popularity or measures of similarity between items or users. This work introduces a novel perspective on movie recommendation that combines a basic quantitative method with a qualitative approach, resulting in a family of mixed character recommender systems. The proposed framework incorporates the use of arguments in favor or against recommendations to determine if a suggestion should be presented or not to a user. In order to accomplish this, Defeasible Logic Programming (DeLP) is adopted as the underlying formalism to model facts and rules about the recommendation domain and to compute the argumentation process. This approach has a number features that could be proven useful in recommendation settings. In particular, recommendations can account for several different aspects (e.g., the cast, the genre or the rating of a movie), considering them all together through a dialectical analysis. Moreover, the approach can stem for both content-based or collaborative filtering techniques, or mix them in any arbitrary way. Most importantly, explanations supporting each recommendation can be provided in a way that can be easily understood by the user, by means of the computed arguments. In this work the proposed approach is evaluated obtaining very positive results. This suggests a great opportunity to exploit the benefits of transparent explanations and justifications in recommendations, sometimes unrealized by quantitative methods.     

An improved collaborative movie recommendation system using computational intelligence

Recommendation systems have become prevalent in recent years as they dealing with the information overload problem by suggesting users the most relevant products from a massive amount of data. For media product, online collaborative movie recommendations make attempts to assist users to access their preferred movies by capturing precisely similar neighbors among users or movies from their historical common ratings. However, due to the data sparsely, neighbor selecting is getting more difficult with the fast increasing of movies and users. In this paper, a hybrid model-based movie recommendation system which utilizes the improved K-means clustering coupled with genetic algorithms (GAs) to partition transformed user space is proposed. It employs principal component analysis (PCA) data reduction technique to dense the movie population space which could reduce the computation complexity in intelligent movie recom-mendation as well. The experiment results on Movielens dataset indicate that the proposed approach can provide high performance in terms of accuracy, and generate more reliable and personalized movie recommendations when compared with the existing methods.     

Effect of viewing a three-dimensional movie with vertical parallax

The vertical parallax of both eyes, caused by tilting the head when watching three-dimensional (3D) television or movies, can induce visual fatigue or visually-induced motion sickness. The purpose of the present study was to investigate levels of discomfort and fatigue and eye movements induced by viewing a 3D movie with vertical parallax. Two experiments were conducted to investigate the relationship between the degree of vertical parallax and the magnitude of the effect. The results indicate that the viewer’s feeling of visual malaise increases as the angle of the screen increases, with respect to the observer’s head, because eye movement does not compensate for the parallax in the vertical direction. Binocular rivalry is believed to occur when viewing a 3D movie with vertical parallax, which may induce even more visual malaise.