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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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Awan, Mazhar Javed, Rafia Asad Khan, Haitham Nobanee, Awais Yasin, Syed Muhammad Anwar, Usman Naseem, and Vishwa Pratap Singh. "A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach." Electronics 10, no. 10 (May 20, 2021): 1215. http://dx.doi.org/10.3390/electronics10101215.
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In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.
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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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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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Derakhti, Arman, Catalina Ramírez-Rivas, and Patricio Esteban Ramírez-Correa. "Streaming or misbehavior, investigation on movie streaming or movie piracy." DYNA 87, no. 215 (November 5, 2020): 102–8. http://dx.doi.org/10.15446/dyna.v87n215.84541.
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Nowadays, movie streaming is ubiquitous amongst the younger population, and it is a supposed substitute for movie piracy. Without prejudice to the previous, reasons of various kinds may cause users to download their favorite movies illicitly. The objective of this study is to identify if movie streaming increases movie piracy. For this purpose, an online survey of potential users of movie streaming services was conducted in Chile. The sample was divided into students and not students, and logit models were used to analyze the data obtained. The results indicate that, on one hand, movie streamers are intentions to engage the download movie illegally, and on the other hand, peer pressure explains movie piracy among students.
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Juliana Dewi, Ni Made Ayu, I. Nyoman Tri Ediwan, and I. Made Suastra. "Language Style in Romantic Movies." Humanis 24, no. 2 (May 27, 2020): 109. http://dx.doi.org/10.24843/jh.2020.v24.i02.p01.
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This study aims to find out the types of language style in romantic movies and what is the dominant type of language style that found in romantic movies. The data were taken from romantic movie entitled The Last Song (2010) and Midnight Sun (2018). The data were collected by using documentation method by applying qualitative audio-visual materials technique.In analysingthe data, this study used both qualitative method and quantitative method and for the presenting data and analysis, formal and informal method were used. The data were analyzed using theory of language style by Joos (1976) and theory of context situation byHymes (1974). The result shows that all types of language style are found in Romantic movie. In The Last Song (2010) movie could be found frozen style, formal style, consultative style, intimate style and casual style. Meanwhile in Midnight Sun (2018) movie there are only formal style, consultative style, intimate style and casual style. The dominant type found in romantic movies is intimate style, while the least is frozen style.
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Sari, Ima Frafika. "The Speech Act of Cartoon Movie: Spongebob Squarepants’ The Movie." Linguists : Journal Of Linguistics and Language Teaching 6, no. 1 (July 13, 2020): 126. http://dx.doi.org/10.29300/ling.v6i1.2854.
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This research aims to reveal: the types of speech acts used by the main character in “Spongebob Squarepants’ the movie and the previous studies in speech act analysis for knowing the way of the directives of speech act appears. It employed descriptive qualitative research in the explaining of speech acts types used by the main character. There is still a lack of research about the analysis of speech act categories in cartoon movie or animation movie, it is substantial to be carried out. The finding of this research is the directives speech act is the most frequently in SpongeBob SquarePants the movie with data 118 or 44,36% from a total of 266 or 100% of the whole data. Then, the similarity data found in the three journals about analysis of speech act with data that the directives speech act is the highest utterance in a cartoon movie.
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Ananda, Fenti Rizki, Diana Chitra Hasan, and Temmy Thamrin. "An Analysis of Translation Procedures Found in the Translation of Movie Subtitle: Zootopia." Journal Polingua : Scientific Journal of Linguistics, Literature and Education 8, no. 1 (March 31, 2019): 11–15. http://dx.doi.org/10.30630/polingua.v8i1.75.
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In this research, the writer attempts to find out the translation procedures in the translation of movie subtitle: Zootopia from English to Indonesian subtitle to determine the intended audience of the movie. The research used a descriptive qualitative method. The data of this research are the utterances or sentences in movie subtitle both English and Indonesian with the source of the data of this research is Zootopia movie. The writer used Indonesian subtitle from the site called subscene where the translators around the world could translate movies into many languages. The data collected by watching the movie with both English and Indonesian subtitles, and categorized the collected data based on each translation procedures of Newmark. The writer figured out that the intended audience is all ages group and the writer also found ten translation procedures used in the movie subtitle: Zootopia. The ten translation procedures are couplets, modulation, paraphrase, reduction, literal translation, cultural equivalent, transference, functional equivalent, naturalisation, transposition/shifts. Based on the findings, it can be concluded that the intended audience of Zootopia movie is all ages group and there are ten from nineteen procedures are found in the movie subtitle: Zootopia.
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Gaikar, Dipak Damodar, Bijith Marakarkandy, and Chandan Dasgupta. "Using Twitter data to predict the performance of Bollywood movies." Industrial Management & Data Systems 115, no. 9 (October 19, 2015): 1604–21. http://dx.doi.org/10.1108/imds-04-2015-0145.
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Purpose – The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India. Design/methodology/approach – This paper uses sentiment analysis and prediction algorithms to analyze the performance of Indian movies based on data obtained from social media sites. The authors used Twitter4j Java API for extracting the tweets through authenticating connection with Twitter web sites and stored the extracted data in MySQL database and used the data for sentiment analysis. To perform sentiment analysis of Twitter data, the Probabilistic Latent Semantic Analysis classification model is used to find the sentiment score in the form of positive, negative and neutral. The data mining algorithm Fuzzy Inference System is used to implement sentiment analysis and predict movie performance that is classified into three categories: hit, flop and average. Findings – In this study the authors found results of movie performance at the box office, which had been based on fuzzy interface system algorithm for prediction. The fuzzy interface system contains two factors, namely, sentiment score and actor rating to get the accurate result. By calculation of opening weekend collection, the authors found that that the predicted values were approximately same as the actual values. For the movie Singham Returns over method of prediction gave a box office collection as 84 crores and the actual collection turned out to be 88 crores. Research limitations/implications – The current study suffers from the limitation of not having enough computing resources to crawl the data. For predicting box office collection, there is no correct availability of ticket price information, total number of seats per screen and total number of shows per day on all screens. In the future work the authors can add several other inputs like budget of movie, Central Board of Film Certification rating, movie genre, target audience that will improve the accuracy and quality of the prediction. Originality/value – The authors used different factors for predicting box office movie performance which had not been used in previous literature. This work is valuable for promoting of product and services of the firms.
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Dhiwar S., Ms Pooja. "Movie Review System using Sentiment Analysis and Social Networking Platforms (SNPs)." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1492–99. http://dx.doi.org/10.22214/ijraset.2021.35308.
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Today online social networking platforms SNPs have become an integral part of or life where we share a lot of information of all the things, we do in life from shopping to movie watching. With ever growing use of SNPs recommendation systems have emerged as a hot trend for applications in e-commerce and digital media. These recommendation systems are useful as well as misguiding. Today digital media use has increased tremendously with increase in internet speeds. But users do not get proper review of a movie and a user is lured to watch a substandard movie which he does not intended to do, thus costing a user time and money. So, there is a need of developing a movie review which will give correct reviews of a digital content like movies so he can only movies which he intends to do. So, we are studying various techniques authored by various authors and create a good movie review system of our own. The first technique we studied and intends to use is movie recommendation system using tweets. The second study is movie recommendation using similarity measures. The third study does find a public shamming using SNP. These techniques are useful and we propose to use some part of each in our new movie review framework by improving the techniques drawbacks. The new framework will be a combination of data from more than one SNP and using natural language processing and machine learning on the data. We are going to use two machine learning algorithms SVM and Naïve Bayes for this purpose. For natural language processing of SNP data, we are going to use OPEN-NLP. We intend to use SNPs such as Twitter and any other movie database like IMDB etc. for data on the movie. The movie will be classified in three classes bad, good and excellent. The results from each algorithm SVM and Naïve bayes will be analyzed for each SNP and try to give user a more accurate movie review by combining all the reviews together and classes accuracy and show overall prediction results with a rating. To get more accurate results for each movie we are going to create a dataset for each movie for demonstration and will not depend on a single combined dataset as keywords for each movie may be different. We are going to combine datasets for each movie from multiple SNPs.
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Khan, Atif, Muhammad Adnan Gul, M. Irfan Uddin, Syed Atif Ali Shah, Shafiq Ahmad, Muhammad Dzulqarnain Al Firdausi, and Mazen Zaindin. "Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics." Scientific Programming 2020 (August 1, 2020): 1–14. http://dx.doi.org/10.1155/2020/5812715.
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Information is exploding on the web at exponential pace, so online movie review is becoming a substantial information resource for online users. However, users post millions of movie reviews on regular basis, and it is not possible for users to summarize the reviews. Movie review classification and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is demanded to summarize the vast amount of movie reviews, and it will allow the users to speedily distinguish the positive and negative aspects of a movie. This study has proposed an approach for movie review classification and summarization. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents, and represent the review documents as a vector space model. Next, the Naïve Bayes algorithm is employed to classify the movie reviews (represented as a feature vector) into positive and negative reviews. For the task of movie review summarization, Word2vec feature extraction technique is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are used to calculate the salience score of each review sentence in clusters. Finally, the top-ranked sentences are chosen based on highest salience scores to produce the extractive summary of movie reviews. Experimental results reveal that the proposed machine learning approach is superior than other state-of-the-art approaches.
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Zhuang, Weiling, Barry Babin, Qian Xiao, and Mihaela Paun. "The influence of movie's quality on its performance: evidence based on Oscar Awards." Managing Service Quality 24, no. 2 (March 4, 2014): 122–38. http://dx.doi.org/10.1108/msq-11-2012-0162.
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Purpose – The purpose of this paper is to develop and empirically test a new framework that shows how different signals of movie quality along with key control variables affect consumers’ post-consumption evaluations, critics’ reviews (CR), and movie box office revenues. Design/methodology/approach – The data set consists of a sample of 332 movies released between 2000 and 2008. Regression was used to test the study hypotheses. Findings – The results suggest that the three signals of movie quality exhibit different effects on three movie performance measures. Of the three cues, the peripheral quality signal is positive related to movie box, moviegoers’ evaluations (ME), and CR. Furthermore, star performance quality is positive related to both ME and CR. Surprisingly, overall quality signal does not display any influence on movie performances. Research limitations/implications – The primary limitation is the use of cross-sectional study design and future research should apply for time-series technique to test the relationships between movie quality signals and movie performances. Practical implications – The findings suggest that consumers and critics evaluate movie qualities based on various movie quality signals. Furthermore, the characteristics of movies also have mixed impacts on movie performances. Movie studios may take these findings into account to produce better movies. Originality/value – This study proposes and empirically tests the impacts of three groups of movie signals – peripheral quality signal, star performance quality signal, and overall quality signal on motion picture performance. This study contributes to service quality literature and signal theory by categorizing different Academy Awards into three groups of quality signals and by empirically testing the proposed relationships.
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Lee, O.-Joun, Seung-Bo Park, Daul Chung, and Eun-Soon You. "Movie Box-office Analysis using Social Big Data." Journal of the Korea Contents Association 14, no. 10 (October 28, 2014): 527–38. http://dx.doi.org/10.5392/jkca.2014.14.10.527.
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Choudhry, Neeta, Jianyun Xie, and Xiaoling Xia. "Big Data Analytics of Movie Rating Predictive System." Journal of Physics: Conference Series 1575 (June 2020): 012063. http://dx.doi.org/10.1088/1742-6596/1575/1/012063.
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Ismail, Nyak Mutia, and Moriyanti Moriyanti. "The overview analysis of the movie Sense and Sensibility." EduLite: Journal of English Education, Literature and Culture 4, no. 1 (February 28, 2019): 45. http://dx.doi.org/10.30659/e.4.1.45-54.
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Movies have been everybody�s favorite across all ages. Some movies are not suitable for certain ages and the parents� assistance is needed during the watching movie process so that they can show the morals conceived in the movie. This study tries to shed lights on the elements contained in �a movie entitled �Sense and Sensibility�, �a movie made based on a novel by Jane Austen. This study was carried out in qualitative approach using visual feature analysis technique. The object of this study was the movie with the duration of 140 minutes. The data obtained were then classified based on its elements such as realism, local color, narratives, and symbolism. The result portrayed that the realism given in this movie is the condition of a noble family who had lost all of their wealth and life had been becoming crude. The local color depicts the social status in the mid-eighteenth century. The story was narrated in the linear plot and finally, the symbolism in this movie exposed much about the calming and melancholic nature of Devonshire.
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Sandya, Isha, and Sri Widati. "THE DIFFERENCE OF EFFECTIVENESS OF ANIMATED AND NON-ANIMATED MOVIES ON THE IMPROVEMENT OF CHILDREN’S KNOWLEDGE AND ATTITUDES ABOUT DENTAL HEALTH." Indonesian Journal of Public Health 14, no. 1 (July 5, 2019): 60. http://dx.doi.org/10.20473/ijph.v14i1.2019.62-70.
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According to Basic Health Research 2013 states that the age group less than 12 years ie age 5-9 years 28.9% suffered dental caries. The aim of this study is to analyze the effectiveness of animated movies and non- animated movies in improving children’s knowledge and behavior concerning dental health. This study was designed based on quasi experimental design. The samples of this study were the students of Class 3A and Class 3B SDN 03 Kepanjen consisting of 63 students. The independent variables of this study were animated movie and non-animated movie concerning dental health while the dependent variables were children’s knowledge and behavior. The collected data were analyzed using Mann Whitney test. The findings of this study show that the respondents are aged between 8 and 10 years old. There is difference in terms of knowledge before and after the students received intervention through animated movie and non-animated movie. Animated movie seems to be more effective in improving the respondents’ knowledge to maintain dental health than non-animated movie as indicated by the average score for animated movie intervention is higher than non-animated movie intervention.
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Topal, Kamil, and Gultekin Ozsoyoglu. "Emotional classification and visualization of movies based on their IMDb reviews." Information Discovery and Delivery 45, no. 3 (August 21, 2017): 149–58. http://dx.doi.org/10.1108/idd-05-2017-0045.
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Purpose The purpose of this study is to detect these reviews’ complex emotions, visualize and analyze them. Movie reviewers’ moviescores and reviews can be analyzed with respect to their emotion content, aggregated and projected onto a movie, resulting in an emotion map for a movie. It is then possible for a moviegoer to choose a movie, not only on the basis of movie scores and reviews, but also on the basis of aggregated emotional outcome of a movie as reflected by its emotion map displaying certain emotion map patterns desirable for the moviegoer. Design/methodology/approach The authors use the hourglass of emotion model to find the emotional scores of words of a review, then they use singular value decomposition to reduce the data dimension into singular scores. Once, they have the emotional scores of reviews, the authors cluster them by using k-means algorithm to find similar emotional levels of movies. Finally, the authors use heat maps to visualize four dimensions in a figure. Findings The authors are able to find the emotional levels of movie reviews, represent them in single scores and visualize them. The authors look the similarities and dissimilarities of movies based on their genre, ranking and emotional statuses. They also find the closest emotion levels of movies to a given movie. Originality/value The authors detect complex emotions from the text and simply visualize them.
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Iyorza, Stanislaus. "Film Content Evaluation: Nollywood in the Mirror of African Movie Academy Awards (AMAA)." Journal of Arts and Humanities 5, no. 9 (October 3, 2016): 75. http://dx.doi.org/10.18533/journal.v5i9.1011.
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<p>Worried by the drastic decline in the quality of content of Nigerian movies as evaluated by critics, this paper analyzes the evaluation of Nigerian movies by the African Movie Academy Awards (AMAA) between 2006 and 2016. The objective is to review the decisions of the AMAA jury and to present the academy’s position on the prospects and deficiencies of the Nigerian Movie industry. The paper employs analytical research approach using both primary and secondary sources to explore assessed contents of the Nollywood movies and how far the industry has fared in the mirror of a renowned African movie assessor like AMAA. This paper assembles data of the awards of AMAA since inception and graphically presents the data. Findings reveal a sharp drop in quality of content of Nigerian movies over the years with a hope of an upsurge as adjudged by AMAA since 2006. The study recommends the private sector’s all round support to Nollywood and the federal government’s training or retraining of filmmakers as well as sustained funding for the steady development of the Nigerian movie industry. </p>
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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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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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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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Sharma, Mugdha, Laxmi Ahuja, and Vinay Kumar. "A Novel Rule based Data Mining Approach towards Movie Recommender System." Journal of information and organizational sciences 44, no. 1 (June 25, 2020): 157–70. http://dx.doi.org/10.31341/jios.44.1.7.
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The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.
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Lavanya, R., and B. Bharathi. "Movie Recommendation System to Solve Data Sparsity Using Collaborative Filtering Approach." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 5 (July 24, 2021): 1–14. http://dx.doi.org/10.1145/3459091.
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With the increase in numbers of multimedia technologies around us, movies and videos on social media and OTT platforms are growing, making it confusing for users to decide which one to watch for. For this, movie recommendation systems are widely used. It has been observed that two-thirds of the films watched on Netflix are the recommended ones to its users. The target of this work is to use implicit feedback given by other users to recommend movies, i.e., ratings given by them. Implicit feedback will help to enhance Data Sparsity as for a replacement logged-in user, the system won't have details of their past liked movies. So, matching the similarity with other users is often a plus point to recommend movies that they would like. The anticipated result will depend upon the positive attitude; i.e., if the predicted rating is high, then it'll be recommended; otherwise it'll not be recommended. The performance of the methodology is measured with accuracy and precision values for different strategies. It gives the best accuracy and highest precision values using Logistic Regression (LR) and lowest recall value as compared to other algorithms. This technique gives an accuracy, precision, and recall value of 81.9%, 69.82%, and 32.5%, respectively, using LR.
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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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Ul Haq, Ijaz, Amin Ullah, Khan Muhammad, Mi Young Lee, and Sung Wook Baik. "Personalized Movie Summarization Using Deep CNN-Assisted Facial Expression Recognition." Complexity 2019 (May 5, 2019): 1–10. http://dx.doi.org/10.1155/2019/3581419.
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Personalized movie summarization is demand of the current era due to an exponential growth in movies production. The employed methods for movies summarization fail to satisfy the user’s requirements due to the subjective nature of movies data. Therefore, in this paper, we present a user-preference based movie summarization scheme. First, we segmented movie into shots using a novel entropy-based shots segmentation mechanism. Next, temporal saliency of shots is computed, resulting in highly salient shots in which character faces are detected. The resultant shots are then forward propagated to our trained deep CNN model for facial expression recognition (FER) to analyze the emotional state of the characters. The final summary is generated based on user-preferred emotional moments from the seven emotions, i.e., afraid, angry, disgust, happy, neutral, sad, and surprise. The subjective evaluation over five Hollywood movies proves the effectiveness of our proposed scheme in terms of user satisfaction. Furthermore, the objective evaluation verifies the superiority of the proposed scheme over state-of-the-art movie summarization methods.
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Vlachos, Michail, and Daniel Svonava. "Recommendation and visualization of similar movies using minimum spanning dendrograms." Information Visualization 12, no. 1 (April 30, 2012): 85–101. http://dx.doi.org/10.1177/1473871612439644.
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Exploration of graph structures is an important topic in data mining and data visualization. This work presents a novel technique for visualizing neighbourhood and cluster relationships in graphs; we also show how this methodology can be used within the setting of a recommendation system. Our technique works by projecting the original object distances onto two dimensions while carefully retaining the ‘backbone’ of important distances. Cluster information is also overlayed on the same projected space. A significant advantage of our approach is that it can accommodate both metric and non-metric distance functions. Our methodology is applied to a visual recommender system for movies to allow easy exploration of the actor–movie bipartite graph. The work offers intuitive movie recommendations based on a selected pivot movie and allows the interactive discovery of related movies based on both textual and semantic features.
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Lee, Seunghwan, Youngsang Cho, Jun Seok Lee, and Donghyeon Yu. "Comparative study of recommender systems using movie rating data." Journal of the Korean Data And Information Science Society 31, no. 6 (November 30, 2020): 975–91. http://dx.doi.org/10.7465/jkdi.2020.31.6.975.
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Chakraborty, Partha, Md Zahidur, and Saifur Rahman. "Movie Success Prediction using Historical and Current Data Mining." International Journal of Computer Applications 178, no. 47 (September 17, 2019): 1–5. http://dx.doi.org/10.5120/ijca2019919415.
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Kumar, Sudhanshu, Kanjar De, and Partha Pratim Roy. "Movie Recommendation System Using Sentiment Analysis From Microblogging Data." IEEE Transactions on Computational Social Systems 7, no. 4 (August 2020): 915–23. http://dx.doi.org/10.1109/tcss.2020.2993585.
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Kim, Jong-Min, Leixin Xia, Iksuk Kim, Seungjoo Lee, and Keon-Hyung Lee. "Finding Nemo: Predicting Movie Performances by Machine Learning Methods." Journal of Risk and Financial Management 13, no. 5 (May 9, 2020): 93. http://dx.doi.org/10.3390/jrfm13050093.
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Analyzing the success of movies has always been a popular research topic in the film industry. Artificial intelligence and machine learning methods in the movie industry have been applied to modeling the financial success of the movie industry. The new contribution of this research combined Bayesian variable selection and machine learning methods for forecasting the return on investment (ROI). We also attempt to compare machine learning methods including the quantile regression model with movie performance data in terms of in-sample and out of sample forecasting.
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Prasetyo, Prasetyo, Meisuri Meisuri, and Juli Rachmadani Hasibuan. "PERSONALITY TRAITS OF BABY IN BABY DRIVER MOVIE." LINGUISTICA 9, no. 1 (April 14, 2020): 243. http://dx.doi.org/10.24114/jalu.v9i1.17764.
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This study aimed at analyzing the kind of traits which represented by Baby in Baby Driver movie by Edgar Wright. The study of Characterization has been thrived in recent centuries. Personality Traits study in a movie is an interesting way to develop and identify some character in movies. The study used descriptive qualitative method. The data of this study were dialogues and statements from Baby which related to the Personality Traits theory in the movie script. The source of the data was Baby Driver movie. The findings of the Big Five theory analyzed by described the utterances and statements from Perlocutionary Act findings. It was found that there were five traits represented by Baby along the movie. They were (1) Introvert/Extrovert, (2) Neuroticism, (3) Conscientiousness, (4) Openness to Experience, and (5) Agreeableness. This study also proved that conflicts always happened in human being, especially teenager. At one time, a person chose to be introvert and in the next day he or she chose to be extrovert. Characterization learning was believed as a tool to differ one character of a movie or real-life individual from another.Keywords: Personality Traits, Speech Act, Perlocutionary Act.
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Husna, Fathayatul, and Ratna Sari Dewi. "Islamic Education Movie: Character Learning Through Nussa-Rara Movie." International Journal of Islamic Educational Psychology 2, no. 1 (June 28, 2021): 36–52. http://dx.doi.org/10.18196/ijiep.v2i1.11209.
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This article examines the Islamic cartoon film Nussa and Rara. The author used Nussa and Rara as a window to see the emergence of Islamic cartoon films. Its strengths are in conveying educational values to children and as a learning medium to build character education for children aged 12 years and under. To explain this phenomenon, the author employed the content analysis method and combined it with relevant data; those were strengthened by many research results and literature studies. Through this method, the author showed that Nussa and Rara movie shares Islamic value in each segment, such as the value of honesty, asking for prayers on time, respecting neighbors, and so on. The author tried to see how every figure, especially Umma as Nussa and Rara’s mother, explains and exemplifies how to be a good Muslim. Through this article, it could be concluded that films are not always judged to have a negative impact on the audience. Even films can contribute as a medium for education and dissemination of Islamic da'wah. Besides, through films, it can also contribute to building good character for children under 12 years of age. Some learning methods that can be used are the many steps or methods that can be planted in children, such as the habituation and storytelling methods. These methods can be used to apply the values of faith, moral values, and worship values. Thus, this article greatly contributes to enriching academic discourse related to character learning for children under 12 years of age. This article concluded that the Nussa and Rara cartoon film really strives to spread Islamic da'wah and build children's character from an early age with daily customs and begin to familiarize children to apply the values of Islamic teachings perfectly.
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Ananda, Ayu, Azizah Salsabila Nugraha, Annisa Rachmah Fujianti, and Eko Susanto. "Movie Induced Tourism in the Young Millennials Tourist Segment." Journal of Tourism Sustainability 1, no. 1 (July 23, 2021): 9–15. http://dx.doi.org/10.35313/jtos.v1i1.1.
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This research is motivated by the considerable potential and market share of millennials as a tourism target market. The promotion strategy of a tourist destination is carried out through innovative media, one of which is through movie media or empirically known as the concept of Movie Induced Tourism. This research was conducted to determine the effect of movies on millennial tourist visits, applying descriptive quantitative research methods. Questionnaires were used as data collection techniques to obtain primary data and through journals and e-book to get secondary data. SEM-PLS has been applied to measure relationships between variables and research models. The results of this study are that movies attributes can have a significant positive effect on personal connections and AIDA Model.
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Sannapu, Suresh, Akshat Singh Parihar, Gaurav Kandwal, and Karan Kakkar. "Importance of Web Based Tools for Promotion of Movies." International Journal of Online Marketing 4, no. 2 (April 2014): 62–73. http://dx.doi.org/10.4018/ijom.2014040105.
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This paper gives a description of the empirical study investigating the impact of promotions using Web based tools by Indian Movie production houses. Evolution of movie promotion starting with use of traditional methods for the first movie to the near dependence on social media to promote the latest movies produced in India has been chronologically presented. Critical role of web based tools and their synchronization with other media tools in contemporary movie promotion has also been elaborated. As Indian film industry is witnessing impeccable advancements in areas like technology and marketing digitalization, this research shows the use of online tools for attracting consumers. The key research objective is to find out the ability of the production houses to attain maximum customer attraction through various online tools like Facebook, Twitter, Blogs and YouTube. Data has been collected both from primary and secondary sources. Regression analysis has been used to depict the relationship between likes, comments and shares with the number of campaigns. Given the huge contribution of movies to Indian economy, ever increasing competition in this industry and increasing popularity of Web based tools; this study aims to benefit multiple stakeholders including movie producers, individual investors and all other entities related to movie making business such as music companies, distributors, exhibitors and single screen owners.
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Hamidah, Ginarti Eka, and Muhamad Sofian Hadi. "ENHANCING LISTENING COMPREHENSION THROUGH FROZEN 2 MOVIE." Journal of Languages and Language Teaching 9, no. 2 (April 22, 2021): 139. http://dx.doi.org/10.33394/jollt.v9i2.3530.
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As an international language, English has become one of the most commonly spoken languages globally, and mastering the English language is necessary for us. This research aimed to answer the research question, namely Enhancing Listening Comprehension Through Frozen 2 Movie. The researcher wants to know whether using the Frozen 2 movie will help students in increasing their listening comprehension or not. This research using a case study of pre-experimental as the research design. The study involved 16 participants of Senior High School around Griya Serpong Asri, Tangerang. The data was derived from pre-test and post-test. The researcher collected it during the research time. The outcome appeared that there is a massive impact on establishing listening comprehension by watching frozen 2 movies as media in the learning process. With the result 0.003 < α (0.05), which means H1 was accepted and H0 was rejected. This research shows several advantages of using Frozen 2 movies as media in the learning process, such as frozen 2 movies as interesting media. Students don’t get bored easily by watching the movie. Also, students can learn body language, facial expressions while listening to the native speaker in the movie.
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Ayuniantari, Ni Putu, Eunike Iona Saptanti, and Eunike Serfina Fajarini. "Family Values in the Movie “A Quiet Place”: A Semiotic Approach." Humanis 24, no. 4 (November 23, 2020): 339. http://dx.doi.org/10.24843/jh.2020.v24.i04.p01.
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There are only few commercially successful horror movies over fifty years since 1970s that win many awards. One of many horror films released in 2018 that left deep impression for audiences is “A Quiet Place”. McClintock (2018) claimed that the movie has earned more than $300M globally, making strides toward becoming one of the biggest-grossing original scary movies ever. “A Quiet Place” is a silence movie directed by John Krasinski. Because of its silence and quietness, the audiences were forced to focus on the nonverbal signs in the movie. The theme of this movie was family values. One of many important films is mise –en-scene as this communicates indirectly to the audiences. The aim of this study was to know how the theme “Family Values” was visualized in the movie. Using qualitative approach and Semiotics analysis method, this study focused on how the signs and mise-en-scene in the film were interpreted based on the researchers’ interpretation using Metz’ Grand Syntagmatique (1974). This study was a desk research and the data were obtained from selected scenes of the movie. The results showed that there were five syntagma category used in the film; i.e. autonomous shot, episodic sequence, scene, alternate syntagma, and descriptive syntagma. The signs that were presented in the film were arranged by using those five syntagma to show the audience about the family value in the movie.
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Syam, Anugrah Febrian, and Andi Qashas Rahman. "Subtitled Films and Learning Listening Comprehension: A Study in Bulukumba, Indonesia." ELT Worldwide: Journal of English Language Teaching 1, no. 1 (October 31, 2014): 59. http://dx.doi.org/10.26858/eltww.v1i1.842.
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This present study is aimed at finding out (1) the possible effect of subtitled and non-subtitled movies on students’ listening achievement, and (2) the difference between using subtitled and non-subtitled movies in students’ listening comprehension. A Comparative study using two groups with a pre-test and post-test design was undertaken in this research. The data were collected using the IELTS listening test. There were two results in the data analysis of IELTS listening test. The first, a general improvement was noted. It was found that both procedures (presenting the movie with or without subtitles) produced a positive effect. Second, the result of movie task data analysis indicated a positive effect for both groups; both groups significantly improved during six weeks. It was revealed that subtitled group exercised a better performance than non-subtitled group. Keywords: Subtitled movies, Non-subtitled movies, The IELTS Listening Test.
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Sembiring, Wulan Angelia, and Ambalegin Ambalegin. "ILLOCUTIONARY ACTS ON ALADDIN MOVIE 2019." JURNAL BASIS 6, no. 2 (November 2, 2019): 279. http://dx.doi.org/10.33884/basisupb.v6i2.1419.
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Illocutionary act is a part of pragmatics studies. Illocutionary acts are acts performed by speakers in saying something (with an appropriate intention and in an appropriate context), The aim of this research were to find out the types of illocutionary act in the Aladdin movie and also to find context underlying illocutionary act in the movie .This method was descriptive qualitative because the data were the utterances of the characters in the Aladdin movie. The research colleting the data with non-participatory methods .There were some steps in collecting the data: downloading the movie script, watching the movie several times, reading and observing the dialogue in the movie .The result of the research there are 30 utterances of directives illocutionary acts . Each of the utterances divided into a part of directive illocutionary act . The data were classified into five namely are , directives (10), assertives (5) declaratives (2), commissives (4), expressives (9).
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Hendriyanto, Restu Dedy, and Yusuf Kurniawan. "FROM FAIRY TALES TO ACTION ADVENTURE MOVIE: THE MAINTENANCE OF WOMEN AS VICTIM OF VIOLENCE IN THE MOVIE SNOW WHITE AND THE HUNTSMAN (2012)." Jurnal Penelitian Humaniora 21, no. 1 (February 1, 2020): 1–14. http://dx.doi.org/10.23917/humaniora.v21i1.7196.
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Gender representation is one of the most prominent subjects of discussion in American society. Fairy tales as one of the first cultural products consumed by the American society are a medium with an influential presence in the issue. They provide gender representations and convey the idea of masculine and feminine- myths of how to be men and women. However, in 20th century, many movies are changing the perspective by presenting women and action and break the traditional myth. In the same year, a movie entitled Snow White and the Huntsman appears as the remake of the written fairy tale Snow White in the form of an action-adventure movie. To move the discussion further, this research examines how the idea of women as victims of violence in the movie is maintained. A number of disciplines in the forms of theory and approach are used to analyze the data. It also applies gender theories and Barthes's semiotic theory in answering the research question. As a result, it is found that the character Snow White and a number of other women characters still become victims of violence. The representations are also still associated with the traditional ideas of women.
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Op.Sunggu, Esra Juniati, and Afriana Afriana. "Flouting Maxims in “Wonder Woman“ Movie." Linguistic, English Education and Art (LEEA) Journal 4, no. 1 (July 20, 2020): 1–12. http://dx.doi.org/10.31539/leea.v4i1.1394.
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This research was made based on the number of people making mistakes in communicating in the social environment. Some errors in communication often result in ambiguity, due to the delivery of unclear information. Related to the topic the researcher made the research that analyze the flouting maxims in Wonder Woman movie and find the reason why the characters flouted the maxims by using the theory of Grice (1975). This research used qualitative descriptive method by Sudaryanto (2015) to analyze data. The results of the research showed that there were 12 data which were flouting maxims namely 1 data flouting maxim of quality, 2 data flouting maxim quantity, 2 data flouting maxim manner and 7 data were flouting maxim relations. The conclusion based on the results of this research is that all the characters in Wonder Woman movie was flouted all of the maxims, it can be seen from the result of analyzed the data, especially in the main character. The most frequently flouting maxim is maxim relation.
Keywords: Communication, Cooperative Principle, Flouting Maxim.
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Jain, Arushi, and Vishal Bhatnagar. "Movie Analytics for Effective Recommendation System using Pig with Hadoop." International Journal of Rough Sets and Data Analysis 3, no. 2 (April 2016): 82–100. http://dx.doi.org/10.4018/ijrsda.2016040106.
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Movies have been a great source of entertainment for the people ever since their inception in the late 18th century. The term movie is very broad and its definition contains language and genres such as drama, comedy, science fiction and action. The data about movies over the years is very vast and to analyze it, there is a need to break away from the traditional analytics techniques and adopt big data analytics. In this paper the authors have taken the data set on movies and analyzed it against various queries to uncover real nuggets from the dataset for effective recommendation system and ratings for the upcoming movies.