• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1611-1614
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• 2015

최근 많은 추천 시스템들이 연구 되고 있으며, 사용자들에게 의사결정을 도와주는 추천시스템에 대한 중요도가 급증하고 있다. 기존의 영화 추천시스템에서는 희박성의 문제가 제기된다. 본 논문에서는 이러한 문제를 보완하고자 사용자가 영화에 대해 남긴 리뷰로부터 영화키워드를 분석하고 분석된 키워드로부터 가중치를 활용한다. 즉 사용자들로부터 영화에 대한 리뷰를 수집하고 리뷰로부터 각 영화 키워드를 분석해 키워드별 가중치를 활용해 이를 기반으로 영화를 추천한다. 그 결과 사용자에게 만족할만한 정보를 제공해 효율성을 높이고, 영화에 대한 개인 리뷰를 반영한 영화추천 시스템을 설계 및 구현해 사용자에게 적절한 영화를 추천한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.261-262
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• 2023

본 연구에서는 감성 분석 기반의 키워드 도출형 영화 리뷰 웹사이트를 개발하였다. 사용자들은 영화에 대한 리뷰를 작성할 때, 자동으로 키워드를 추출하는 기능을 활용하여 다양하면서도 빠르게 정보를 얻을 수 있다. 사용자가 작성한 리뷰를 시스템에 입력하면, 내부적으로 ChatGPT를 활용하여 텍스트를 분석하고 키워드를 추출한다. 이를 통해 사용자는 별다른 노력 없이도 키워드를 통해 영화의 장르, 감독, 배우, 플롯 요소 등 다양한 정보를 빠르게 확인할 수 있다. 추출된 키워드는 저장되어 시각화에 활용되며, 사용자들은 리뷰에 대한 원하는 정보를 쉽게 얻을 수 있다. 개발된 키워드 도출형 영화 리뷰 웹사이트는 사용자들에게 빠르고 다양한 정보를 제공하며, 영화 관련 결정을 내리는 데에 도움을 줄 것으로 기대된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.1-6
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• 2019

This study visualized users reaction about movies based on keywords with high frequency. For this work, we collected data of movie reviews on . A total of six movies were selected, and we conducted the work of data gathering and preprocessing. Semantic network analysis was used to understand the relationship among keywords. Also, NetDraw, packaged with UCINET, was used for data visualization. In this study, we identified the differences in characteristics of review contents regarding each movie. The implication of this study is that we visualized movie reviews made by sentence as keywords and explored whether it is possible to construct the interface to check users' reaction at a glance. We suggest that further studies use more diverse movie reviews, and the number of reviews for each movie is used in similar quantities for research.

• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.85-94
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• 2014

This study examined the characteristics of movie reviews based on emotional expressions, using the structural analysis. Major results were as follows; firstly, the most cited emotional expression was 'fun'. Fun was the important discriminator for evaluating movies. Secondly, cluster analysis results found that although Korean movies were clustered by many emotional expressions such as fun, immersion and impression, foreign movies were grouped by joust an emotional expression including fun. Internet users tended to divide foreign movie into two kinds of movies such as fun movie and boring movies.

• Information Systems Review
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• v.16 no.3
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• pp.113-134
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• 2014

Since a movie is an experience goods, purchase can be decided upon preliminary information and evaluation. There are ongoing researches on what impact online reviews might have on movie revenues. Whereas research in the past was focused on the effect of online reviews. The influence of online reviews appears to be significant in products like a movie because it is difficult to evaluate the feature prior to "consuming" the product. Since an online review is regarded to be objective, consumers find it more trustworthy. Contrary to prior research focused on movie review ratings and volume, we focus moves on movie features related specific reviews. This research proposes a predictive model for movie revenue generation. We decided 15 criteria to classify movie features collected from online reviews through the online review mining and made up feature keyword list each criterion. In addition, we performed data preprocessing and dimensional reduction for data mining through factor analysis. We suggest the movie revenue predictive model is tested using discriminant analysis. Following the discriminant analysis, we found that online review factors can be used to predict movie popularity and revenue stream. We also expect using this predictive model, marketers and strategic decision makers can allocate their resources in more parsimonious fashion.

• 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 Information Processing Society Conference
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• 2021.05a
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• pp.316-318
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• 2021

영화 수익에 있어 영화의 흥행 여부는 중요한 영향을 끼친다. 영화 흥행 요인은 영화 산업의 규모가 커지면서 많은 제작사들 및 투자자들이 고려해야 하는 사항이 되었다. 따라서 영화의 흥행을 예측하기 위한 많은 모델이 연구되었다. 본 연구의 목적은 선행연구에서 흥행에 유의미한 영향을 끼친다고 밝혀진 스크린 수, 감독명, 제작사명 등의 내재적인 속성과 더불어 온라인 구전 변수를 사용하여 영화 흥행 예측 모델을 만드는 것이다. 이때 기사 수, 블로그 수와 같이 온라인 구전의 크기를 나타내는 변수들을 사용하는 대신 개봉 후 첫 주간의 관람객 리뷰를 텍스트마이닝을 이용하여 전체 리뷰 중 긍정 리뷰의 비율에 따라 점수를 매긴 후 독립변수로 사용한다. 그 후, 데이터 마이닝 기법을 활용하여 만든 모델에 앞서 언급한 독립변수를 입력 값으로 사용하여 영화의 흥행을 예측한다. 최종적으로 의사결정트리와 로지스틱회귀를 수행한 결과 영화 흥행에 영향을 주는 독립변수를 찾고 모델의 성능을 평가하였다. 로지스틱회귀의 결과 관객 수, 평점이 영화의 흥행에 특히 유의한 영향을 끼치는 변수로 선정되었고 리뷰 역시 유의한 변수로 선정되었다. 이때 만들어진 모델은 약 90%의 높은 수준의 정확도를 보여주었다. 의사결정트리의 결과 관객 수가 가장 중요한 변수로 선정되었다.

• 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.

• Journal of Korea Entertainment Industry Association
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• v.13 no.7
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• pp.1-13
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• 2019

This study tried to explore the contents of film critics' reviews reported in the press. Based on fifty nine Korean movies with over 100,000 audience in 2017, this study collected 1113 reviews from fifty five movies with the exception of four without reviews. This study focused on the correlation between film's overall quality and four evaluation items such as directing, acting, story, and the visual. Examining the difference in the report timing of the review, the length of the review, and the intensity of the opinion, this study also analyzed the relationship between the internal aspects of reviews and box office performance. According to the results, the valence of critics' reviews was generally positive. Looking at the difference of reporting time, this valence was higher in the week before release than in the release week of film. The evaluation items of reviews were highly covered both before movie release and in the opening week. These were significantly declined in the second week of release. In the relationship between the number of reviews by each movie and box office performance, a positive correlation was found.