• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.167-176
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• 2013

User-created text data are increasing rapidly caused by development of social media. In opinion mining, User's opinions are extracted by analyzing user's text. A primary goal of sentiment analysis as a branch of opinion mining is to extract user's opinions from a text that is required to build a list of emotion terms. In this paper, we built a list of emotion terms to analyse a sentiment of social media using Facebook as a representative social media. We collected data from Facebook and selected a emotion terms, and measured the dimensions of valence and activation through a survey. As a result, we built a list of 267 emotion terms including the dimension of valence and activation.

• The Pure and Applied Mathematics
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• v.24 no.4
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• pp.227-241
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• 2017

The study investigates the stock market using emotion index calculated from SMD based on investors' emotion. In the VAR anlaysis, we find that the correlation between the KOSPI200 return and emotion score sum is highest in 2- or 3- day lag. This study concludes that explanatory power of the SMD emotion index is limited in explaining the Korean stock market yet.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.425-440
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• 2012

As the scale of the internet grows, the amount of subjective data increases. Thus, A need to classify automatically subjective data arises. Sentiment classification is a classification of subjective data by various types of sentiments. The sentiment classification researches have been studied focused on NLP(Natural Language Processing) and sentiment word dictionary. The former sentiment classification researches have two critical problems. First, the performance of morpheme analysis in NLP have fallen short of expectations. Second, it is not easy to choose sentiment words and determine how much a word has a sentiment. To solve these problems, this paper suggests a combination of using web-scale data and a statistical approach to sentiment classification. The proposed method of this paper is using statistics of words from web-scale data, rather than finding a meaning of a word. This approach differs from the former researches depended on NLP algorithms, it focuses on data. Hadoop and MapReduce will be used to handle web-scale data.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.127-140
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• 2015

In this study, we aim to analyze the relationship between the centrality in the social network and the sentiment of medial web forum users. In recent, many people use online resources to obtain health and wellness information especially social media resources. In the medial web forum, people give and receive informational supports and emotional supports and this interaction forms the social network. We analyze the social network, derive node characteristics in terms of centrality and compare the centrality index and the sentiment score derived from users' messages. We found that as more people express their emotion, they possess higher central position in the network. Further, people who express positive emotion in their messages have higher central position in the network than people who have negative emotion. This study will help to identify influentials of emotional supports to others and finally to control the depression of Alzheimer's disease patients and their related ones.

• Science of Emotion and Sensibility
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• v.27 no.1
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• pp.69-80
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• 2024

This study aimed to gain valuable insights into the performance and user satisfaction of applications (apps) through a thorough analysis of Instagram user reviews collected from Google Play. The study utilized text mining and sentiment analysis techniques and systematically identified emotions and opinions embedded in user reviews to deeply understand the areas of improvement and user experiences of the app. It analyzes how Instagram reviews reflect the diverse experiences of users and how they reveal the strengths and weaknesses of the app. For this purpose, sentiment analysis using the naive Bayes algorithm was conducted, and the results were expected to aid in the improvement of Instagram's services. In addition, the study aimed to assist developers in better understanding and utilizing user feedback, ultimately contributing to enhanced user satisfaction. This study explored the complex relationship between social media usage patterns and user opinions by seeking ways to provide a better user experience through these insights.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.34-36
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• 2022

In the existing text sentiment analysis models, the entire text is usually directly modeled as a whole, and the hierarchical relationship between text contents is less considered. However, in the practice of sentiment analysis, many texts are mixed with multiple emotions. If the semantic modeling of the whole is directly performed, it may increase the difficulty of the sentiment analysis model to judge the sentiment, making the model difficult to apply to the classification of mixed-sentiment sentences. Therefore, this paper proposes a sentiment analysis model BHGCN that considers the text hierarchy. In this model, the output of hidden states of each layer of BERT is used as a node, and a directed connection is made between the upper and lower layers to construct a graph network with a semantic hierarchy. The model not only pays attention to layer-by-layer semantics, but also pays attention to hierarchical relationships. Suitable for handling mixed sentiment classification tasks. The comparative experimental results show that the BHGCN model exhibits obvious competitive advantages.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1537-1542
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• 2017

Emotion plays a key role in both generating and understanding narrative. In this article we analyzed the emotions represented in a movie script based on 8 emotion types from the wheel of emotions by Plutchik. First we conducted manual emotion tagging scene by scene. The most dominant emotions by manual tagging were anger, fear, and surprise. It makes sense when the film script we analyzed is a thriller-genre. We assumed that the emotions around the climax of the story would be heightened as the tension grew up. From manual tagging we could identify three such duration when the tension is high. Next we analyzed the emotions in the same script using Python-based NLTK VADERSentiment tool. The result showed that the emotions of anger and fear were most matched. The emotion of surprise, anticipation, and disgust, however, scored lower matching.

• 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.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.25-31
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• 2018

Online news has replaced the traditional newspaper and has brought about a profound transformation in the way we access and share information. News websites have had the ability for users to post comments for quite some time, and some have also begun to crowdsource reactions to news articles. The field of sentiment analysis seeks to computationally model the emotions and reactions experienced when presented with text. In this work, we analyze more than 100,000 news articles over ten categories with five user-generated emotional annotations to determine whether or not these reactions have a mathematical correlation to the news body text and propose a simple sentiment analysis algorithm that requires minimal preprocessing and no machine learning. We show that it is effective even for a morphologically complex language like Korean.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.545-550
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• 2024

In a society undergoing rapid change, modern individuals are facing various stresses, and there's a noticeable increase in mental health treatments for children as well. For the psychological well-being of children, it's crucial to swiftly discern their emotional states. However, this proves challenging as young children often articulate their emotions using limited vocabulary. This paper aims to categorize children's psychological states into four emotions: depression, anxiety, loneliness, and aggression. We propose a method for constructing an emotion dictionary tailored for children based on assessments from child psychology experts.