• Human Ecology Research
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• v.57 no.2
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• pp.185-199
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• 2019

This study examined the effects of considered attributes when purchase on consumption attitudes and consumption happiness as well as compared the effects for sensible consumption and sentimental consumption. Data was collected from 373 consumers in 20s and 30s from November 15th to 20th in 2017. IBM SPSS 25.0 was used for basic statistical analyses and for paired t-tests, and lavaan 0.6-3 package and semTools package in R 3.3.3(2017-03-06) was used for structural equation modeling. The results of this study are summarized as follows. First, there were almost no differences in product types between sentimental consumption and sensible consumption. Second, consumption attitudes toward sensible consumption were more positive than sensible consumption; however consumption happiness from sentimental consumption was higher than from sensible consumption. Third, considered attributes when making purchases were divided into subjective, objective, and symbolic with the effects of these attributes on consumption attitudes and consumption happiness analyzed by structural equation modeling. Regardless of sentimental or sensible consumption, objective selection attributes have a positive effect on consumption attitude, but subjective selection attributes have a positive effect on consumption happiness. This study implies that sentimental consumption has a positive value for contemporary consumers and that it should be counted as a feasible consumption activity to enhance consumption happiness.

• Design Convergence Study
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• v.14 no.4
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• pp.1-16
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• 2015

This study aims to understand distribution of the sentimental words on each genre and find relationship between box office performance and sentimental words in movie review using 673 movies that have more than 1,000 reviews. For the analysis, crawling movie reviews and made data was composed movie genre, movie name, sales, attendance, screen, normal attendance, 7 sentimental words. For analysis results, we used correlation analysis and Parallel coordinates. As a results, First, the highest box office value of the genre is comedy and the lowest box office value of the genre is horror through analyze box office on each genre. Secondly, Movie genre of fantasy feel a lot of boring emotion and Movie genre of SF feel a lot of anger emotion even if 'Happy' and 'Surprise' have highest sentiment value on every genre. Third, We found 'Anger' increase sentimental value when 'Disgust' increase sentimental value and 'Surprise' decrease sentimental value when 'Happy' increase sentimental value through analyze correlation relationship between sentimental words using total data. Fourth, We found 'Happy' have linear relationship between box office and 'Fear' have non-linear relationship between box office through analyze sentimental words according to box office performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1422-1427
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• 2013

This paper proposed a movie retrieval system based on sentimental keywords extracted from user's movie reviews. At first, sentimental keyword dictionary is manually constructed by applying morphological analysis to user's movie reviews, and then keyword weights in the dictionary are calculated for each movie with TF-IDF. By using these results, the proposed system classify sentimental categories of movies and rank classified movies. Without reading any movie reviews, users can retrieve movies through queries composed by sentimental keywords.

• Journal of English Language & Literature
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• v.64 no.1
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• pp.117-133
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• 2018

This article examines Laurence Sterne's Sentimental Journey in the context of eighteenth-century British travel literature. While literary critics generally read Sterne's work as a sentimental novel, contemporary readers initially interpreted the text as a travel narrative. It is my argument that travel writing, particularly the motion entailed in travelling, plays a significant role in Sterne's critical examination of sympathy and its cultural function during this period. By narrating in great detail his narrator Yorick's mobility and the effects it has on his sentimental encounters, Sterne illustrates how sympathy is not only difficult to activate and therefore requires added stimulation in the form of motion, but also does not necessarily result in charitable actions, a moral failure that is dramatized by the literal distance Yorick maintains from the objects of his sympathy. Calling to mind the figurative distance that constitutes an integral part of Adam Smith's formulation of sympathy in The Theory of Moral Sentiments, the distance Yorick establishes through his travels indicates sympathy's failure to bridge the emotional and socioeconomic distance between individuals, thereby highlighting sympathy's limitations as a moral instrument. I argue that by using Yorick's repeated acts of sympathy to explore the problems of sentimentalism, Sterne both draws from and innovates the tradition of employing imaginary voyages to engage in philosophical inquiries.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.100-106
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• 2021

Over the past few decades, natural language processing research has not made much. However, the widespread use of deep learning and neural networks attracted attention for the application of neural networks in natural language processing. Sentiment analysis is one of the challenges of natural language processing. Emotions are things that a person thinks and feels. Therefore, sentiment analysis should be able to analyze the person's attitude, opinions, and inclinations in text or actual text. In the case of emotion analysis, it is a priority to simply classify two emotions: positive and negative. In this paper we propose the deep learning based sentimental analysis system according to various optimizer that is SGD, ADAM and RMSProp. Through experimental result RMSprop optimizer shows the best performance compared to others on IMDB data set. Future work is to find more best hyper parameter for sentimental analysis system.

• The Journal of Society for e-Business Studies
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• v.25 no.1
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• pp.13-33
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• 2020

This study proposes a Korean sentimental analysis algorithm that utilizes a letter-unit embedding and convolutional neural networks. Sentimental analysis is a natural language processing technique for subjective data analysis, such as a person's attitude, opinion, and propensity, as shown in the text. Recently, Korean sentimental analysis research has been steadily increased. However, it has failed to use a general-purpose sentimental dictionary and has built-up and used its own sentimental dictionary in each field. The problem with this phenomenon is that it does not conform to the characteristics of Korean. In this study, we have developed a model for analyzing emotions by producing syllable vectors based on the onset, peak, and coda, excluding morphology analysis during the emotional analysis procedure. As a result, we were able to minimize the problem of word learning and the problem of unregistered words, and the accuracy of the model was 88%. The model is less influenced by the unstructured nature of the input data and allows for polarized classification according to the context of the text. We hope that through this developed model will be easier for non-experts who wish to perform Korean sentimental analysis.

• Management & Information Systems Review
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• v.17
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• pp.1-24
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• 2005

The purpose of this study is to figure out the effects of advertisements (functional value, sentimental value, self-expressing value) depending on the gender in regard of the attitude towards the advertisement, brand attitude, purchase intention, and the characteristic and quality of the product. From the study on the university students, there was a meaningful interaction among the independent variables in regard of attitude towards advertisement, brand attitude, purchase intention, and characteristic of the product in the four dependent measurements used for the index of advertisement effects. In terms of the attitude towards advertisements, brand attitude, purchase intention, and the characteristic of the product according to the gender and value suggestion, functional convenience was more influential for men compared to the sentimental convenience and self-expressing value. On the other hand, self-expressing value was more influential for women in terms of the advertisement effect and the characteristic of the product. The main effects depending on the gender were common in four dependent values such as attitude towards advertisement, brand attitude, purchase intention and characteristic of the product, and the average of all values was higher from women. Thereby, it can be said that women show more positive advertisement effects in terms of attitude and purchase than men. The main effects on the value suggestion were meaningfully indicated in advertisement attitude, brand attitude, and characteristic of the product except the purchase intention. Also, the functional and self-expressing value made better advertisement effect, while the sentimental value showed a comparatively lower advertisement effect. In terms of the sentimental value, a comparatively low advertisement effect was shown statistically compared to the functional and self-expressing value in all dependent values, but there was no big difference depending on the gender. That is, in terms of the sentimental value, a separate value suggesting advertisement can be more influential when it is combined with the functional value for men, and for women if it is combined with self-expressing value.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.89-96
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• 2017

Recently, a number of researches actively focus on the contents and sensitivity of information distributed through SNS as smartphones and SNS gained its popularity. In this paper, we collected online news data about SW education, extracted words after morphological analysis, and analyzed emotions of collected news data by calculating sentimental score of each news datum. Also, the accuracy of the calculated sentimental score was examined. As a result, the number of news related to 'SW education' in the collection period was about 189 per month, and the average of sentimental score was 0.7, which signifies the news related to 'SW education' was emotionally positive. We were positive about the importance of SW education and the policy implementation, but there were negative views on the specific method for the realization. That is, a lack of SW education environment and its education method, a problem related to improvement of SW developers and improvement of their labor conditions, and increase of private education in coding were the factors for the negative viewers.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1167-1173
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• 2015

The development of information and communication technology like SNS, blogs, and bulletin boards, was provided a variety of places where you can express your thoughts and comments and allowing Big Data to grow, many people reveal the opinion of the social issues in SNS such as Twitter. In this study, we would like to pre-built sentimental dictionary about social issues and conduct a sentimental analysis with structured dictionary, to gather opinions on social issues that are created on twitter. The data that I used is "bikini", "nakkomsu" including tweet. As the result of analysis, precision is 61% and F1- score is 74%. This study expect to suggest the standard of dictionary construction allowing you to classify positive/negative opinion on specific social issues.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.257-264
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• 2019

Recently, deep neural network based approaches have shown a good performance for various fields of natural language processing. A huge amount of training data is essential for building a deep neural network model. However, collecting a large size of training data is a costly and time-consuming job. A data augmentation is one of the solutions to this problem. The data augmentation of text data is more difficult than that of image data because texts consist of tokens with discrete values. Generative adversarial networks (GANs) are widely used for image generation. In this work, we generate sentimental texts by using one of the GANs, CS-GAN model that has a discriminator as well as a classifier. We evaluate the usefulness of generated sentimental texts according to various measurements. CS-GAN model not only can generate texts with more diversity but also can improve the performance of its classifier.