• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.41-48
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• 2015

On movie review sites, movie ratings are determined by netizens' subjective judgement. This means that inconsistency between ratings and opinions from netizens often occurs. To solve this problem, this paper proposes sentiment sentence sets which affect movie evaluation, and apply sets to comments to infer ratings. Creation of sentiment sentence sets is consisted of two stages, construction of sentiment word dictionary and creation of sentiment sentences for sentiment estimation. Sentiment word dictionary contains sentimental words and its polarities included in reviews. Elements of sentiment sentences are combined with movie related noun and predicate from words sentiment word dictionary. In this study, to make correspondence between polarity of sentiment sentence and sentiment word dictionary, sentiment sentences which have different polarity with sentiment word dictionary are removed. The scores of comments are calculated by applying averages of sentiment sentences elements. The result of experiment shows that sentence scores from sentiment sentence sets are closer to reflect real opinion of comments than ratings by netizens'.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.367-375
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• 2018

The study aims to determine which groups leave longer(more active) online reviews(comments) on the film by separating groups, one that satisfied with the movie while the other group dissatisfied with the movie. The data used were rating scores and reviews(comments) from Naver Movie API, and break-even point data provided by Korea Film Commission. We analyzed the relationship between movie rating and review length, before and after movie opening, the characteristics of review length according to the box office, and whether the movie rating affects the review length.

• Annual Conference on Human and Language Technology
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• 2011.10a
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• pp.37-41
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• 2011

본 논문에서는 최근 많은 관심을 받고 있는 Opinion Mining으로서 사용자들의 자연어 형태의 영화평 문장을 분석하여 자동으로 평점을 예측하는 시스템을 제안한다. 제안 시스템은 영화평 분석에 적합한 어휘 자질, 감정 자질, 가치 자질 및 기타 자질들을 추출하고, 10점 척도의 영화평의 평점을 10개의 범주로 가정하여, 커널모델인 다중 범주 Support Vector Machine (SVM) 모델을 이용하여 높은 성능으로 영화평의 평점을 범주 분류한다.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.1
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• pp.17-24
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• 2020

In this paper, we present a movie rating prediction architecture based on a convolutional neural network (CNN). Our prediction architecture extends TextCNN, a popular CNN-based architecture for sentence classification, in three aspects. First, character embeddings are utilized to cover many variants of words since reviews are short and not well-written linguistically. Second, the attention mechanism (i.e., squeeze-and-excitation) is adopted to focus on important features. Third, a scoring function is proposed to convert the output of an activation function to a review score in a certain range (1-10). We evaluated our prediction architecture on a movie review dataset and achieved a low MSE (e.g., 3.3841) compared with an existing method. It showed the superiority of our movie rating prediction architecture.

• Management & Information Systems Review
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• v.34 no.3
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• pp.161-177
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• 2015

Currently, the influence of the Internet portal sites that can make it quick and easy to contact the vast amount of information is increasing. Users can connect the Internet through a portal to obtain information, such as communication between Internet users, which can be used to meet a variety of purposes. People are exposed to a variety of information from other users in the search for a movie and get information. The impact on the reviews and ratings with the limited number of characters of the film allows users to form a relationship to the movie, decide whether you want to see the movie or find another movie. but, the user can not read the whole movie review. When user see the overall evaluation, the user can receive the correct information. This research conducted a study on the prediction of the rating by the use of review data. Information of reviews, is divided into two main areas: the"fact" and "opinion". "Fact" is to convey the dispassionate information and "Opinion" is, to represent the user's feelings. In this study, we built sentiment dictionary based on the assessment and evaluation of the online review and applied to evaluate other movies. In the comparative study with a simple emotion evaluation technique, we found the suggested algorithm got the more accurate results.

• Proceedings of the Korea Contents Association Conference
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• 2012.05a
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• pp.325-326
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• 2012

세 얼간이는 국내 포탈 사이트에 평점 등록한 네티즌만 2만여 명이 넘었으며, 네이버 9.43, 다음 9.7, 네이트 9.5 등의 평점으로 역대 영화 평점 순위 1위를 차지했다. 반지의 제왕 같은 판타지 블록버스터, 타이타닉, 대부 등 세계적인 흥행으로 전설이 되어버린 작품들의 기록을 뛰어넘는 것으로 전 세계 부동의 흥행 1위 아바타를 뛰어넘고, 700억 원이 넘는 수익을 창출했다. 세 얼간이가 성공할 수 있었던 것은 몇 가지로 볼 수 있는데 영화 전편에 걸친 스토리텔링과 비선형적 스토리구성, 모션의 활용, 사운드의 활용, 이벤트의 활용 등 인터랙션과 스토리텔링의 효과가 우수하였다고 사료한다.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.296-310
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• 2017

The present study analyzed the factors affecting the box office performance of movies in the Chinese market in 2010~2014. A total of 499 movies were selected for the final analyses. Based on the previous studies, genre, actor/actress power, director power, sequel, remake, release period, award, online evaluation, distributor power, and production area were chosen as independent variables. Regression analyses showed that most of the independent variables except for distributor power were found to affect box office performance of the movies.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.357-366
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• 2018

This study analyzed the factors affecting box office performance of 200 movies released at the Chinese theater in 2015. The results showed that main actor power, online rating, production power, and Chinese film were sighificant factors which influenced box office, while the distribution power, genre, IP utilization and integration of production and distribution were insignificant. These results mean that online marketing factors such as the popularity index of the main actors evaluated on the internet and the online rating are affecting box office performances in Chinese theaters.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.125-134
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• 2021

With the rapid development of the film industry, the number of films is significantly increasing and movie recommendation system can help user to predict the preferences of users based on their past behavior or feedback. This paper proposes a movie recommendation system based on the latent factor model with the adjustment of mean and bias in rating. Singular value decomposition is used to decompose the rating matrix and stochastic gradient descent is used to optimize the parameters for least-square loss function. And root mean square error is used to evaluate the performance of the proposed system. We implement the proposed system with Surprise package. The simulation results shows that root mean square error is 0.671 and the proposed system has good performance compared to other papers.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.19-33
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• 2022

With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.