• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.393-402
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• 2016

To compensate for limited the satisfaction survey currently conducted by Korea Metrological Administration (KMA), a sentiment analysis via a social networking service (SNS) can be utilized. From 2011 to 2014, with the sentiment analysis, Twitter who had commented 'KMA' had collected, then, using $Na{\ddot{i}}ve$ Bayes classification, we were classified into three sentiments: positive, negative, and neutral sentiments. An additional dictionary was made with morphemes appeared only in the positive, negative, and neutral sentiments of basic $Na{\ddot{i}}ve$ Bayes classification, thus the accuracy of sentiment analysis was improved. As a result, when sentiments were classified with a basic $Na{\ddot{i}}ve$ Bayes classification, the training data were reproduced about 75% accuracy rate. Whereas, when classifying with the additional dictionary, it showed 97% accuracy rate. When using the additional dictionary, sentiments of verification data was classified with about 75% accuracy rate. Lower classification accuracy rate would be improved by not only a qualified dictionary that has increased amount of training data, including diverse keywords related to weather, but continuous update of the dictionary. Meanwhile, contrary to the sentiment analysis based on dictionary definition of individual vocabulary, if sentiments are classified into meaning of sentence, increased rate of negative sentiment and change in satisfaction could be explained. Therefore, the sentiment analysis via SNS would be considered as useful tool for complementing surveys in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.217-245
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• 2016

The purpose of this study is to verify how 7 sentiment domains extracted through sentiment analysis from social media have an influence on business performance. It consists of three phases. In phase I, we constructed the sentiment lexicon after crawling 45,447 pieces of VOC (Voice of the Customer) on 26 auto companies from the car community and extracting the POS information and built a seven-sensitive domains. In phase II, in order to retain the reliability of experimental data, we examined auto-correlation analysis and PCA. In phase III, we investigated how 7 domains impact on the market share of three major (GM, FCA, and VOLKSWAGEN) auto companies by using linear regression analysis. The findings from the auto-correlation analysis proved auto-correlation and the sequence of the sentiments, and the results from PCA reported the 7 sentiments connected with positivity, negativity and neutrality. As a result of linear regression analysis on model 1, we indentified that the sentimental factors have a significant influence on the actual market share. In particular, not only posotive and negative sentiment domains, but neutral sentiment had significantly impacted on auto market share. As we apply the availability of data to the market, and take advantage of auto-correlation of the market-related information and the sentiment, the findings will be a huge contribution to other researches on sentiment analysis as well as actual business performances in various ways.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2169-2190
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• 2022

The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.285-307
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• 2019

In 2011, the Korean Medical Association revised the name of schizophrenia to remove the social stigma for the sick. Although it has been about nine years since the revision of the terminology, no studies have quantitatively analyzed how much social awareness has changed. Thus, this study investigates the changes in social awareness of schizophrenia caused by the revision of the disease name by analyzing Naver news articles related to the disease. For text analysis, LDA topic modeling, TF-IDF, word co-occurrence, and sentiment analysis techniques were used. The results showed that social awareness of the disease was more negative after the revision of the terminology. In addition, social awareness of the former term among two terms used after the revision was more negative. In other words, the revision of the disease did not resolve the stigma.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.347-373
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• 2023

As the significance of ESG (Environmental, Social, and Governance) management amplifies in driving sustainable growth, this study delves into and compares ESG trends and interrelationships from both corporate and societal viewpoints. Employing a combination of Latent Dirichlet Allocation Topic Modeling (LDA) and Semantic Network Analysis, we analyzed sustainability reports alongside corresponding social media datasets. Additionally, an in-depth examination of social media content was conducted using Joint Sentiment Topic Modeling (JST), further enriched by Semantic Network Analysis (SNA). Complementing text mining analysis with the assistance of ChatGPT, this study identified 25 different ESG topics. It highlighted differences between companies aiming to avoid risks and build trust, and the general public's diverse concerns like investment options and working conditions. Key terms like 'greenwashing,' 'serious accidents,' and 'boycotts' show that many people doubt how companies handle ESG issues. The findings from this study set the foundation for a plan that serves key ESG groups, including businesses, government agencies, customers, and investors. This study also provide to guide the creation of more trustworthy and effective ESG strategies, helping to direct the discussion on ESG effectiveness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.164-172
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• 2018

This paper examines the relevance of pre-production and storytelling in big data analysis and, focusing on JTBC's Man to Man series, looks at how the drama's storytelling should be structured. In this study, we conducted text mining on blogs focused on a particular topic to read the viewer's thoughts on pre-produced dramas and on 67 blogs written about Pre-Production Dramas from 2016.12.15 to 2017.12.15. Also, we conducted sentiment analysis about the Man to Man series, which is not only a pre-production drama, but also has storytelling issues. The blog text extraction and text mining were analyzed using the OutWit Hub and the R, and the tools.provided by social metrics were used to make sentiment analyses of the larger data. Sentiment analysis revealed that the viewers of the Man to Man series did not agree with the romance between Kim Sul-woo and Cha Do-ha, due to the lack of reality in the female characters. Therefore, it was concluded that it is crucial to increase the reality of the characters in order to increase the audience's empathy. These studies will continue to be necessary, because they will form the basis for digitally driven storytelling studies and will provide valuable materials for conducting predictions and instructions in the cultural content industry.

• Journal of Korea Trade
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• v.24 no.8
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• pp.39-62
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• 2020

Purpose - Korean exports account for a vast proportion of Korean GDP, and large volumes of Korean products are sold in the United States. Identifying and characterizing actual and potential product hazards related to Korean products is critical to safeguard Korean export trade, as severe quality issues can impair Korea's reputation and reduce global consumer confidence in Korean products. In this study, we develop country-of-origin-based product risk analysis methods for social media with a specific focus on Korean-labeled products, for the purpose of safeguarding Korean export trade. Design/methodology - We employed two social media datasets containing consumer-generated product reviews. Sentiment analysis is a popular text mining technique used to quantify the type and amount of emotion that is expressed in the text. It is a useful tool for gathering customer opinions regarding products. Findings - We document and discuss the specific potential risks found in Korean-labeled products and explain their implications for safeguarding Korean export trade. Finally, we analyze the false positive matches that arise from the established dictionaries that were used for risk discovery and utilize these classification errors to suggest opportunities for the future refinement of the associated automated text analytic methods. Originality/value - Various studies have used online feedback from social media to analyze product defects. However, none of them links their findings to trade promotion and the protection of a specific country's exports. Therefore, it is important to fill this research gap, which could help to safeguard export trade in Korea.

• Information Systems Review
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• v.22 no.4
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• pp.59-74
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• 2020

This paper investigates the asymmetric effect of social sentimental on an individual stock price return. For this purpose, four companies such as POSCO, Korean Electricity, AMORE PACIFIC, KIA Motors are chosen from KOSPI listed companies in terms of dataperspective. The main estimation results are as follows: the positive opinions affect only the stock prices return of three companies while the negative opinions affect all of the companies. It shows that positive or negative texts give asymmetric effect on stock price return and the effect of negative opinions is bigger than that of positive opinions. The results imply that investors are more sensitive to the negatives since they have the tendency of loss aversion. Also, it indicates that subjective opinion on SNS can be used as the proxy for the investment sentiment.

• Journal of the Korea Safety Management & Science
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• v.19 no.4
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• pp.169-182
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• 2017

Predicting a box office gross in the film industry is an important goal. Many works have analyzed the elements of a film making. Previous studies have suggested several methods for predicting box office such as a model for distinguishing people's reactions by using a sentiment analysis, a study on the period of influence of word-of-mouth effect through SNS. These works discover that a word of mouth (WOM) effect through SNS influences customers' choice of movies. Therefore, this study analyzes correlations between a box office gross and a ratio of people reaction to a certain movie by extracting their feedback on the film from before and after of the film opening. In this work, people's reactions to the movie are categorized into positive, neutral, and negative opinions by employing sentiment analysis. In order to proceed the research analyses in this work, North American tweets are collected between March 2011 and August 2012. There is no correlation for each analysis that has been conducted in this work, hereby rate of tweets before and after opening of movies does not have relationship between a box office gross.

• Journal of Distribution Science
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• v.19 no.4
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• pp.25-35
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• 2021

Purpose: This study aims to analyze the factors affecting customer satisfaction in the customer reviews of omni-channel, posted on Internet blogs, cafes, and YouTube using text mining analysis. Research, data, and Methodology: In this study, frequency analysis is performed and the LDA (Latent Dirichlet Allocation) is used to analyze social big data to respond to reviewers' reaction to the recently opened omni-channel shopping reviews by L Shopping Company. Additionally, based on the topic analysis, we conduct a sentiment analysis on purchase reviews and analyze the characteristics of each topic on the positive or negative sentiments of omni-channel app users. Results: As a result of a topic analysis, four main topics are derived: delivery and events, economic value, recommendations and convenience, and product quality and brand awareness. The emotional analysis reveals that the reviewers have many positive evaluations for price policy and product promotion, but negative evaluations for app use, delivery, and product quality. Conclusions: Retailers can establish customized marketing strategies by identifying the customer's major interests through text mining analysis. Additionally, the analysis of sentiment by subject becomes an important indicator for developing products and services that customers want by identifying areas that satisfy customers and areas that evoke negative reactions.