• 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.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.121-128
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• 2015

A given product's online product reviews build up to form largely positive or negative reviews or mixed reviews that include both the positive and negative reviews. While the homogeneously positive or negative reviews help readers identify the generally praised or criticized product, the mixed reviews with minority opinions potentially contain valuable information about the product. We present a method of retrieving minority opinions from the online product reviews using the skewness of positive/negative reviews. The proposed method first classifies the positive/negative product reviews using a sentiment dictionary and then calculates the skewness of the classified results to identify minority reviews. Minority review retrieval experiments were conducted on smartphone and movie reviews, and the F1-measures were 24.6% (smartphone) and 15.9% (movie) and the accuracies were 56.8% and 46.8% when the individual reviews' sentiment classification accuracies were 85.3% and 78.8%. The theoretical performance of minority review retrieval is also discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.202-207
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• 2010

In this paper, we present an automatic sentiment classification method for on-line movie reviews that do not contain explicit sentiment rating scores. For the sentiment polarity classification, positive or negative, we use a Support Vector Machine classifier based on syllable kernel that is an extended model of string kernel. We give some experimental results which show that proposed syllable kernel model can be effectively used in sentiment classification tasks for on-line movie reviews that usually contain a lot of grammatical errors such as spacing or spelling errors.

• Design Convergence Study
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• v.15 no.2
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• pp.347-368
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• 2016

Recently, researches for the word of mouth(WOM) imply that consumers use WOM informations of products in their purchase process. This study suggests methods using opinion mining and visualization to understand consumers' opinion of each goods and each markets. For this study we conduct research that includes developing domain ontology based on reviews confined to "movie" category because people who want to have watching movie refer other's movie reviews recently, and it is analyzed by opinion mining and visualization. It has differences comparing other researches as conducting attribution classification of evaluation factors and comprising verbal dictionary about evaluation factors when we conduct ontology process for analyzing. We want to prove through the result if research method will be valid. Results derived from this study can be largely divided into three. First, This research explains methods of developing domain ontology using keyword extraction and topic modeling. Second, We visualize reviews of each movie to understand overall audiences' opinion about specific movies. Third, We find clusters that consist of products which evaluated similar assessments in accordance with the evaluation results for the product. Case study of this research largely shows three clusters containing 130 movies that are used according to audiences'opinion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.4975-4983
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• 2012

Commercial and Art movies have different characteristics even when they belong to the same 'movie' category. This study investigated the differential effects of reviewer professionalism and expectation about a movie on the intention to watch the movie. Based on the survey on young potential movie goers, we found that movie types indeed have significant effects. Specifically, in the case of art movie, people had high level of intention to watch when they were exposed to professional reviews. We found that utilitarian expectation mediates between reviewer professionalism and intention to watch for art movie. On the other hand, in the case of commercial movie, people had high level of intention to watch when they were exposed to amateur reviewers. We found that hedonic expectation mediates between reviewer professionalism and intention to watch for art movie. In result, this study contributes to our knowledge to movie industry through showing that reviewer professionalism have different impacts on commercial and art movies.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.581-587
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• 2013

In this paper, we propose a method of sentiment classification which uses Levenshtein distance. We generate BOW(Bag-Of-Word) applying Levenshtein daistance in sentiment features and used it as the training set. Then the machine learning algorithms we used were SVMs(Support Vector Machines) and NB(Naive Bayes). As the data set, we gather 2,385 reviews of movies from an online movie community (Daum movie service). From the collected reviews, we pick sentiment words up manually and sorted 778 words. In the experiment, we perform the machine learning using previously generated BOW which was applied Levenshtein distance in sentiment words and then we evaluate the performance of classifier by a method, 10-fold-cross validation. As the result of evaluation, we got 85.46% using Multinomial Naive Bayes as the accuracy when the Levenshtein distance was 3. According to the result of the experiment, we proved that it is less affected to performance of the classification in spelling errors in documents.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.201-207
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• 2022

In this paper, we propose a movie recommendation model that uses the users' ratings as well as their reviews. To understand the user's preference from multicriteria perspectives, the proposed model is designed to apply attribute-based sentiment analysis to the reviews. For doing this, it divides the reviews left by customers into multicriteria components according to its implicit attributes, and applies BERT-based sentiment analysis to each of them. After that, our model selectively combines the attributes that each user considers important to CF to generate recommendation results. To validate usefulness of the proposed model, we applied it to the real-world movie recommendation case. Experimental results showed that the accuracy of the proposed model was improved compared to the traditional CF. This study has academic and practical significance since it presents a new approach to select and use models in consideration of individual characteristics, and to derive various attributes from a review instead of evaluating each of them.

• Asia Marketing Journal
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• v.26 no.1
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• pp.23-30
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• 2024

Firms often engage in manipulating online reviews as a promotional activity to influence consumers' evaluation on their products. With the prevalence of the promotional activities, consumers may notice and discount the reviews generated by the promotional activities. Discounting the firm-generating reviews may cause systematic measurement errors in the valence variable and lead to a negative bias when estimating the effect of consumers' organic reviews on demand. To correct the bias, this study proposes including product-specific bias-correction terms representing the proportion of extreme reviews in analysis. For illustration, the proposed method is applied to a demand model for data of movies released in South Korea. The results confirm a negative bias in the estimate of the valence sensitivity of demand. The negative bias potentially leads to an underestimation of the magnitude of the contagion effect through social interactions, a key component of evaluating the value of a satisfied consumer.

• 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.