• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Human Ecology Research
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• v.61 no.3
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• pp.389-399
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• 2023

Sustainable fashion products have different characteristics from typical fashion products. Therefore, this study focuses on shoes while exploring the expansion and development of sustainable fashion consumption as well as consumers' perceptions of the sustainability approaches practiced by shoe companies. In-depth interviews were conducted with 24 consumers, who had purchased sustainable shoes, in order to understand their purchase decision-making process and consumption characteristics, using the seven stages of the EBM model. In the "need recognition" stage, the survey participants' social background and family influences were categorized as macro factors, while their personal background influences were categorized as micro factors. In the "evaluation of alternatives" stage, participants reconfirmed whether or not to make a purchase based on the product's properties, such as price, brand value, and offered services. In the "purchase" stage, participants' purchase channels were determined according to their preferences as well as the selection pattern they followed until the final purchase within the chosen channel. In the "consumption" stage, the start of product ownership coincides with the start of using the products after making a purchase. In the "post-purchase assessment" stage, higher positive experiences led to a higher repurchase intention of sustainable shoes, while negative experiences caused participants to defer consumption and made them experience a sense of guilt for failing to consume sustainably. During the "post-purchase behavior" stage, which focused on the categories that the customers prioritized, many participants spread information about sustainable fashion to specific individuals through active online WOM behavior.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of Korean Library and Information Science Society
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• v.54 no.1
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• pp.191-220
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• 2023

This research attempts to identify how members of an online community collaboratively engaged with particular social information behaviors and accomplished a defined collective action. While responding to the Sewol Ferry tragedy, MissyUSA members quickly communicated and mobilized a collective action, a full-page ad campaign in The New York Times. As a follow up study, this secondary analysis quantitatively analyzes the primary data from a previous study to explore potential relationships or underlying factors among the various identified information behaviors. In this study, nineteen of the previously identified information behaviors were analyzed using exploratory factor analysis, yielding a total of eight factors. The two major factors of shared representation/collective identification and mobilizing resources verified the findings of the previous study and are in line with the findings typical of political science. The three factors of collaborative decision-making, reaction to tension, and brainstorming were factors that maximized communication and mobilization online, without any face-to-face communication or physical organization. Three emergent factors of outburst of dissent, boycott, and planning explained how members used negative emotions of anger, referential information for boycott, and incubated next collective actions. Through exploratory factor analysis, this study verifies and expands on the findings of the previous study by identifying several emergent factors that relate to the collaborative information behaviors of an online community engaged in a collective action.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1103-1110
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• 2013

This paper proposes a method of identifying ownership of personal information in Social Network Service. In detail, the proposed method automatically decides whether any location information mentioned in twitter indicates the publisher's residence area. Identifying ownership of personal information is necessary part of evaluating risk of opened personal information online. The proposed method uses a set of decision rules that considers 13 features that are lexicographic and syntactic characteristics of the tweet sentences. In an experiment using real twitter data, the proposed method shows better performance (f1-score: 0.876) than the conventional document classification models such as naive bayesian that uses n-gram as a feature set.

• Journal of Distribution Science
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• v.13 no.2
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• pp.75-83
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• 2015

Purpose - This study aims to identify the effects of communication cues, anonymity, and social presence on group polarization in computer-mediated communication (CMC) settings. Extant literature has introduced some theoretical backgrounds of social presence and SIDE (Social Identity model of Deindividuation Effects) to explain the effects of communication cues and anonymity. The concept of social presence emphasized the mediating role on communication cues and anonymity. However, most literature did not measure social presence and compare group polarization of all condition groups. This does not sufficiently explain the result of group polarization. Research design, data, and methodology - We believe that the direct impact of anonymity on group polarization can provide a more admissible and clearer explanation for the results. In addition, this study categorizes anonymity into two levels, as anonymity of group and anonymity of self. To justify the anonymity view, a laboratory experiment was conducted. The experiment was conducted in communication cues settings (visual cue; without visual cue) and anonymity settings (identified; anonymous). Each of the four settings has 10 groups consisting of five subjects each (total 200 subjects). The subjects are undergraduates from a large university, majoring in business. All experimental procedures and calculations of choice shift and preference change follow the literature. Results - First, the removal of visual cues does not produce a significant impact on group polarization, which cannot be explained by the social presence view. Second, the anonymous condition does not significantly affect group polarization, which also cannot be explained by the social presence view. However, the anonymous condition directly affects group polarization. Specifically, anonymity of self has a stronger effect on group polarization than anonymity of group. The result explains about the leading factor affecting group polarization. This study examines another view of how computer-mediated communication may be associated with group polarization. The process and outcome data from the experiment reveal that group polarization is not affected by level of social presence, but by level of anonymity. Group discussions conducted with visual cue CMC setting and identified CMC setting result in weaker group polarization. Conversely, group discussions conducted without visual cue CMC setting and anonymous CMC setting lead to stronger group polarization. The results of the study have the following implications. First, they provide clues for business organizations to design the most appropriate media conditions and preemptive social conditions to implement when making group decisions through CMC, to maximize achievements, generate amicable agreements, or actively share information. Second, this study can be useful in analyzing different adverse effects generated through Internet use. Conclusions - This research can help explain discussions and decision-making actions on Internet forums, which have recently increased, as well as providing a foundational basis in newly establishing policies for the forums. Finally, it should be noted that many other factors such as group size, topics, and group history may affect group polarization. These should be examined in future studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1153-1158
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• 2018

Online customer evaluations and social media information among a variety of information sources are critical for businesses as it influences the customer's decision making. There are limitations on the time and money that the survey will ask to identify a variety of customers' needs and complaints. The customer review data at online shopping malls provide the ideal data sources for analyzing customer sentiment about their products. In this study, we collected product reviews data on the smartphone of Samsung and Apple from Amazon. We applied five classification algorithms which are used as representative sentiment analysis techniques in previous studies. The five algorithms are based on support vector machines, bagging, random forest, classification or regression tree and maximum entropy. In this study, we proposed entropy score which can comprehensively evaluate the performance of classification algorithm. As a result of evaluating five algorithms using an entropy score, the SVMs algorithm's entropy score was ranked highest.

• Information Systems Review
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• v.21 no.2
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• pp.29-50
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• 2019

Social media is divided into Owned Media, operated by companies according to information sources, and Earned Media, which third parties produce contents. Social media research developing the logic that brand-related content in social media increases awareness of potential customers and positively changes brand attitudes, resulting in increased sales and business performance. However, there are limitations in previous researches that can not fully explain the difference of media synergy effect according to the information source of social media. it is very important for the consumer to integrate media management because consumers are more likely to choose appropriate media information for the information needed at each decision making stage. The purpose of this study is to analyze the effect of eWOM of review site and social media (owned media and earned media) on movie sales. To do this, we collected 3,589 review data from films released in 2017. The results of the study showed that eWOM of review site, social media (owned media and earned media) had a positive effect on movie sales. However, it was found that the effect of moderating eWOM of review site was different between the owned media and the earend media.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.389-392
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• 2020

The growth in social media, blogs and restaurant listing directories have led to increasing customer reviews about restaurants, their quality of food items and services available on the internet. These user reviews offer a massive amount of valuable information that can be used for various decision-making purposes. Currently, most food recommendation sites provide recommendation scores about restaurants rather than food items of the restaurant and the provided recommendation scores may be biased since they are calculated only from user reviews listed only in their sites. Usually, people wants a reliable recommendation about foods, not restaurant. In this paper, we present a reliable Korean food items rating method; we first extract food items by applying NER technique to restaurant reviews collected from many Korean restaurant recommendation web sites, blogs and web data. Then, we apply lexicon-based sentiment analysis on collected user reviews and predict people's opinions as sentiment polarity scores (+1 for positive; -1 for negative; 0 for neutral). Finally, by taking average of all calculated polarity scores about a food item, we obtain a rating to individual menu items of the restaurant. The proposed food item rating is more reliable since it does not depend on reviews of only one site.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.37-48
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• 2023

Purpose - This study aims to explore the influence of emotional discrepancies among investors on the cryptocurrency market. It focuses on how varying emotions affect market dynamics such as volatility and trading volume in the context of Bitcoin trading. Design/methodology/approach - This study involves analyzing data from Bitcointalk.org, consisting of 57,963 posts and 2,215,776 responses from November 22, 2009, to December 31, 2022. Tools used include the Linguistic Inquiry and Word Count (LIWC) software for classifying emotional content and the Python Pattern library for sentiment analysis. Findings - The results show that heterogeneous emotional feedback, whether positive or negative, significantly influences Bitcoin's intraday volatility, skewness, and trading volume. These findings are more pronounced when the underlying emotion in the feedback is amplified. Research implications or Originality - This study underscores the significance of emotional factors in financial decision-making, especially within the realm of social media. It suggests that investors and market strategists should consider the emotional landscape of online forums when making investment choices or formulating market strategies. The research also paves the way for future studies regarding the behavioral impact of emotions on the cryptocurrency market.