• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.9-17
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• 2021

Humans are social animals that have gained information or social interaction through dialogue. In conversation, the mood of the word can change depending on the sensibility of one person to another. Relationships between characters in films are essential for understanding stories and lines between characters, but methods to extract this information from films have not been investigated. Therefore, we need a model that automatically analyzes the relationship aspects in the movie. In this paper, we propose a method to analyze the relationship between characters in the movie by utilizing deep learning techniques to measure the emotion of each character pair. The proposed method first extracts main characters from the movie script and finds the dialogue between the main characters. Then, to analyze the relationship between the main characters, it performs a sentiment analysis, weights them according to the positions of the metabolites in the entire time intervals and gathers their scores. Experimental results with real data sets demonstrate that the proposed scheme is able to effectively measure the emotional relationship between the main characters.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.267-272
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• 2021

This study's purpose is to investigate the effects of reading movie scripts aloud in non-face-to-face general English courses on international students' English ability in the COVID-19 era. A general English class was delivered once a week for 15 weeks to 47 international students at a Seoul-based university. The animated movie Tangled and its script were used as learning materials. Biweekly, students had to watch video lectures using the university's learning management system(LMS) and read scripts aloud through Zoom. In the video lectures, the teacher went over specific vocabulary and interpreted the movie scripts in easy Korean. For the second activity through Zoom, international students read the movie script aloud individually and in groups. The post-test revealed significant improvements in both reading and writing, as compared to the pre-test. Through the study's survey, participants exhibited positive attitudes in affective domains(understanding, satisfaction, interest, and recommendation).

• Hwankyungkyoyuk
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• v.20 no.2
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• pp.25-35
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• 2007

The purpose of this study was to investigate the effect on the achievement of knowledge by animation-based learning program(ALP) in environmental education. The topic covered by ALP was "meaning and kinds of alien animals and plants". Four classes were selected and divided into two groups. The one was control group, and the other was treatment. 121 female the eighth graders(60 control and 61 ALP students) were piloted. Whereas control students learned using Powerpoint presentation-based materials, ALP students learned using web-based flash animations and animated database with images and movie clips. The score of knowledge achievement of ALP students was compared to control group students using ANCOVA, and then the score of ALP students was significantly higher than that of control group students(p<0.05). The ALP using animations and animated database was effective on the understanding of the alien animals and plants.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.101-106
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• 2023

In this busy world actually stress is continuously grow up in research and monitoring social websites. The social interaction is a process by which people act and react in relation with each other like play, fight, dance we can find social interactions. In this we find social structure means maintain the relationships among peoples and group of peoples. Its a limit and depends on its behavior. Because relationships established on expectations of every one involve depending on social network. There is lot of difference between emotional pain and physical pain. When you feel stress on physical body we all feel with tensions, stress on physical consequences, physical effects on our health. When we work on social network websites, developments or any research related information retrieving etc. our brain is going into stress. Actually by social network interactions like watching movies, online shopping, online marketing, online business here we observe sentiment analysis of movie reviews and feedback of customers either positive/negative. In movies there we can observe peoples reaction with each other it depends on actions in film like fights, dances, dialogues, content. Here we can analysis of stress on brain different actions of movie reviews. All these movie review analysis and stress on brain can calculated by machine learning techniques. Actually in target oriented business, the persons who are working in marketing always their brain in stress condition their emotional conditions are different at different times. In this paper how does brain deal with stress management. In software industries when developers are work at home, connected with clients in online work they gone under stress. And their emotional levels and stress levels always changes regarding work communication. In this paper we represent emotional intelligence with stress based analysis using machine learning techniques in social networks. It is ability of the person to be aware on your own emotions or feeling as well as feelings or emotions of the others use this awareness to manage self and your relationships. social interactions is not only about you its about every one can interacting and their expectations too. It about maintaining performance. Performance is sociological understanding how people can interact and a key to know analysis of social interactions. It is always to maintain successful interactions and inline expectations. That is to satisfy the audience. So people careful to control all of these and maintain impression management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.263-270
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• 2014

This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short cultural goods. Therefore, in order to produce stable profits, marketing costs before opening as well as the number of screen after opening need a plan. To fulfill this plan, the demand for the product and the calculation of economic profit scale should be preceded. The cases of existing researches, as a variable for predicting, primarily use the factors of competition of the market or the properties of the film. However, the proportion of the potential audiences who purchase the goods is relatively insufficient. Therefore, in this paper, in order to consider people's perception of a movie, Twitter was utilized as one of the survey samples. The existing variables and the information extracted from Twitter are defined as off-line and on-line element, and applied those two elements in machine learning by combining. Through the experiment, the proposed predictive techniques are validated, and the results of the experiment predicted the chance of successful film with about 95% of accuracy.

• Journal of Power Electronics
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• v.10 no.6
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• pp.643-646
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• 2010

An interactive web-based teaching tool for a power semiconductor course at the University of Central Florida is presented in this paper. A novel approach is introduced using Technology Aided Design Tools (TCAD) to generate time-lapsed 2D semiconductor device cross-section embedded in a webpage using $Adobe^{(R)}$ Flash (web design tool) platform to create interactive movies that demonstrate complex device physical phenomenon. Students can step through the interactive movies forward, backward, pausing, or looping. Each step represents a giving bias condition. Current-voltage plots are represented along with the semiconductor device and a visual point is placed on the IV curve to indicate the current bias conditions. The changes are then reflected in the 2D cross-section movie area and the IV plot. This tool was implemented in a classroom setting to augment the lectures or for discovery learning.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• Journal of the Korean earth science society
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• v.25 no.8
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• pp.748-753
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• 2004

The purpose of study is to develop the web-contents for web based instruction (WBI) with the Science Fiction Movie (SF) and investigate the effects of application to the science learning. The results are as follows. 1) Science fiction movies for the earth science classes of middle school were selected, that is the Armageddon, the Apollo13 and the Total Recall. They are related to the chapters, the Earth and the Stars in the 8th grade science textbook. 2) The developed WBI materials were applied to the science classes of the 2nd grade in middle school through the Internet Line. and then the changes of attitude on science and recognition on using the science fiction movies to the class were analyzed. The results show that the science fiction movies have so much related to the contents of science learning and the developed WBI materials are effective to the science classes(p<0.05), but no meaningful (p>0.05) to the attitude on the science excluding the students' interest to science learning.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.49-55
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• 2019

In the age of information, newly produced knowledge is mostly written in English. Therefore, there has been a strong demand for English language learning in the EFL context. However, most EFL learners possess a lack of interest and motivation in the text-based reading class. In this educational context, film is one of the most widely used materials in English reading classes considering that modern learners are predominantly familiar with various audiovisual materials. The purpose of this study is to investigate how Korean EFL learners experienced in the film-based reading class. Specifically, this study aims to analyze the EFL students' perceptions about the class and learning strategies that they used during the class. In order to comprehensively interpret the EFL learners' experiences in the classroom, a coding system consisting of five categories was developed: report, emotion, reflection, evaluation, future plans. The results of data analysis showed that the use of movies in English reading classes had positive effects on reading comprehension and inference of word meaning. The most frequently used learning strategies were affective strategies which helped them control their emotion, attitude, motivations and values, whereas memorization strategies were rarely used. In this respect, this study suggests that the use of movies in the EFL reading classroom encourage students' attention and help them obtain and activate schema which is useful in gaining a better understanding of text-based reading materials.