• Advanced Industrial SCIence
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• v.3 no.2
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• pp.1-7
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• 2024

This paper deals with the topic of quantification of soft power through emotional analysis. Sentiment analysis refers to the process of detecting and analyzing emotions or emotions in various data such as text, voice, and images. Therefore, in this paper, we explored the methodology and significance of how soft power can be quantified through emotional analysis. Soft power refers to the ability of a country or organization to influence the behavior of another country or organization in a desired direction. It is built by soft factors such as culture, values, and political system rather than military or economic means. Additionally, sentiment analysis is being used as a useful tool to measure and understand these soft areas.

• The Journal of the Korea Contents Association
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• v.12 no.6
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• pp.151-159
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• 2012

The purpose of this thesis is to figure out the mechanism that how can be shown the aesthetic distances of novel in the film. At discussion of the view point, novel can be told by two factors which are 'who is teller' and 'who is watcher' but in the film, novel's narration is divided into visual point and auditive point. And I will consider the phenomenon on the part of this difference. Next, I will argue about difference between novel and film from the Park Jongwon's aesthetic distances which interpreted Lee Munyeol's work. This thesis is going to observe that how the film adapted three types of view point and how that related the subject of the original novel. For this thesis, I tried to track 'the distances' between figure and identity, and reader and author. Also I did approach that how can be accepted the problem of 'aesthetic distance according to identity' based on this novel in the film and novel's text by reader. This study make a proposal or analysis to the differences between novels and films in terms of narrative point of view. Although it is shown by dividing into each chapter in novel and on connectivity in film, this paper finds out that both film and novel are shown the subject of reader's difference of the view point about 'author and director's identity'.

• Journal of the military operations research society of Korea
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• v.26 no.1
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• pp.56-69
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• 2000

A frequency assignment problem on time division duplex system is considered. Republic of Korea Army (ROKA) has been establishing an infrastructure of tactical communication (SPIDER) system for next generation and it will be a core network structure of system. VHF system is the backbone network of SPIDER, that performs transmission of data such as voice, text and images. So, it is a significant problem finding the frequency assignment with no interference under very restricted resource environment. With a given arbitrary configuration of communications network, we find a feasible solution that guarantees communication without interference between sites and relay stations. We formulate a frequency assignment problem as an Integer Programming model, which has NP-hard complexity. To find the assignment results within a reasonable time, we take a genetic algorithm approach which represents the solution structure with available frequency order, and develop a genetic operation strategies. Computational result shows that the network configuration of SPIDER can be solved efficiently within a very short time.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.81-88
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• 2021

Recently, many videos such as online lecture videos caused by COVID-19 have been generated. However, due to the limitation of working hours and lack of cost, they are only a part of the videos with subtitles. It is emerging as an obstructive factor in the acquisition of information by deaf. In this paper, we try to develop a system that automatically generates subtitles using voice recognition and generates subtitles by separating sentences using the ending and time to reduce the time and labor required for subtitle generation.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.39-49
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• 2010

Provision of in-vehicle warning information is of keen interest since it can be effectively used to prevent traffic accident on the road. This study evaluates the effectiveness of information provision modalities based on kansei engineering. Various warning information scenarios using different modalities are devised for the evaluation. Statistical data analysis techniques including factor analysis, correlation analysis, and the general linear model are used to assess the user's affect for information modalities. The evaluation result shows that the provision of visual information consisted of 'text and pictogram' leads to higher understandability. The combination of beep sound and voice message' was identified as a more effective modality for auditory warning. In addition, the red color for the blinking warning signal was preferred by users.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.10
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• pp.189-200
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• 2022

The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments of consumers concerning gig economy services (Grab, Foodpanda and Airbnb) through the analysis of social media. First, Vader Lexicon was used to classify the comments into positive, negative, and neutral sentiments. Then, the comments were further classified into three machine learning algorithms: Support Vector Machine, Light Gradient Boosted Machine, and Logistic Regression. Results suggested that gig economy services in Malaysia received more positive sentiments (52%) than negative sentiments (19%) and neutral sentiments (29%). Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. The results of this study proved the power of text mining and sentiment analysis in extracting business value and providing insight to businesses. Additionally, it aids gig managers and service providers in understanding clients' sentiments about their goods and services and making necessary adjustments to optimize satisfaction.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.48-57
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• 2019

The purpose of this study is to understand the intention of the inquirer from the single text type question in Goal-oriented dialogue. Goal-Oriented Dialogue system means a dialogue system that satisfies the user's specific needs via text or voice. The intention analysis process is a step of analysing the user's intention of inquiry prior to the answer generation, and has a great influence on the performance of the entire Goal-Oriented Dialogue system. The proposed model was used for a daily chemical products domain and Korean text data related to the domain was used. The analysis is divided into a speech-act which means independent on a specific field concept-sequence and which means depend on a specific field. We propose a classification method using the word embedding model and the CNN as a method for analyzing speech-act and concept-sequence. The semantic information of the word is abstracted through the word embedding model, and concept-sequence and speech-act classification are performed through the CNN based on the semantic information of the abstract word.

• Speech Sciences
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• v.13 no.4
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• pp.187-200
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• 2006

The speech synthesis technology is widely used and its application area is also being broadened to an automatic response service, a learning system for handicapped person, etc. However, the sound quality of the speech synthesizer has not yet reached to the satisfactory level of users. To make a synthesized speech, the existing synthesizer generates rhythms only by the interval information such as space and comma or by several punctuation marks such as a question mark and an exclamation mark so that it is not easy to generate natural rhythms of people even though it is based on mass speech database. To make up for the problem, there is a way to select rhythms after processing language from a higher level information. This paper proposes a method for generating tags for controling rhythms by analyzing the meaning of sentence with speech situation information. We use the Systemic Functional Grammar (SFG) [4] which analyzes the meaning of sentence with speech situation information considering the sentence prior to the given one, the situation of a conversation, the relationship among people in the conversation, etc. In this study, we generate Semantic Speech Control Tag (SSCT) by the result of SFG's meaning analysis and the voice wave analysis.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.141-158
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• 2017

Along with IT development, customers are getting more easily to express their opinions using various IT channels. In this situation, complaint management is a pressing issue for companies to acquire and maintain loyal customers with low cost. Most of previous studies have investigated customer complaint information by quantitative variables such as demographic information, transaction information, or complaint frequency, but studies focusing on qualitative aspects of complaint information are limited. Therefore, this paper considers the possibility for customers to leave even when they complain occasionally or briefly. This paper analyzes the quantitive aspects as well as the qualitative aspects using sentiment analysis with Exit-voice theory. The dataset contains 268,364 inquiries of 46,235 customers obtained from a contact center of a private security company in Korea. This paper carries out logistic regression and the results imply that the customers's explicit response and their implicit sentiment have different effect on customers leave. This study is expected to provide useful suggestions for the effective complaint management.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.11-19
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• 2024

The purpose of this study was to analyze user reviews of running applications using text mining. This study used user reviews of Nike Run Club and Runkeeper in the Google Play Store using the selenium package of python3 as the analysis data, and separated the morphemes by leaving only Korean nouns through the OKT analyzer. After morpheme separation, we created a rankNL dictionary to remove stopwords. To analyze the data, we used TF, TF-IDF and LDA topic modeling in text mining. The results of this study are as follows. First, the keywords 'record', 'app', and 'workout' were identified as the top keywords in the user reviews of Nike Run Club and Runkeeper applications, and there were differences in the rankings of TF and TF-IDF. Second, the LDA topic modeling of Nike Run Club identified the topics of 'basic items', 'additional features', 'errors', and 'location-based data', and the topics of Runkeeper identified the topics of 'errors', 'voice function', 'running data', 'benefits', and 'motivation'. Based on the results, it is recommended that errors and improvements should be made to contribute to the competitiveness of the application.