• 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.

• Korean Journal of Cognitive Science
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• v.19 no.1
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• pp.1-15
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• 2008

Analyzing of dependency relation among clauses is one of the most critical parts in parsing Korean sentences because it generates severe ambiguities. To get successful results of analyzing dependency relation, this task has been the target of various machine learning methods including SVM. Especially, kernel methods are usually used to analyze dependency relation and it is reported that they show high performance. This paper proposes an expression and a composit kernel for dependency analysis of Korean clauses. The proposed expression adopts a composite kernel to obtain the similarity among clauses. The composite kernel consists of a parse tree kernel and a liner kernel. A parse tree kernel is used for treating structure information and a liner kernel is applied for using lexical information. the proposed expression is defined as three types. One is a expression of layers in clause, another is relation expression between clause and the other is an expression of inner clause. The experiment is processed by two steps that first is a relation expression between clauses and the second is a expression of inner clauses. The experimental results show that the proposed expression achieves 83.31% of accuracy.

• The KIPS Transactions:PartA
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• v.10A no.2
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• pp.93-100
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• 2003

With the advent of distributed computing and Web service technologies, lots of users have been requiring services that can conveniently obtain and/or support well-assembled information based on Web. For this reason, we are to construct Automatic Linking Support Systems for generating Web-based integrated information and supporting retrieval information according to user's various requirements. Our system organization is based on client/server system. A server environment consisted of automatic linking engine that can provide lexical analyzing, query processing and integrated document generating functions, and databases that are made of dictionaries, image and URL contents. Also, client environments consisted of Web editor that can generate integrated documents and Web helper that can retrieve them via automatic linking engine and databases. For client's user-friendly interfaces, web editor and helper programs can directly execute by down leading from a server without setup them before inside clients. For reducing server's overheads, Parts of server's executing modules are distributed to clients on which they can be executing. As an implementation of our system, we use the JDK 1.3, SWING for user interfaces like Web editor and helper, RMI mechanism for interaction between clients and a server, and SQL server 7.0 for database development, respectively. Finally, we showed the access procedures of automatic document linking engine and databases from Web editor or Web helper, and results appearing on their screens.

• Korean Journal of Cognitive Science
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• v.17 no.2
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• pp.103-118
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• 2006

The main purpose of this paper is to investigate the effects of discourse topic and morpho-syntactic verbal information on the resolution of null pronouns in the Korean narrative text within the framework of the construction-integration theory (Kintsch, 1988, Singer & Kintsch, 2001, Graesser, Gernsbacher, & Goldman. 2003). For the purpose of this paper, two conditions were designed: an explicit condition with both a consistently maintained discourse topic and the person-specific verb modals on one hand, and a neutral condition with no discourse topic or morpho-syntactic information provided, on the other. We measured the reading tines far the target sentence containing a null pronoun and the question response times for finding an antecedent, and the accuracy rates for finding an antecedent. During the experiments each passage was presented at a tine on a computer-controlled display. Each new sentence was presented on the screen at the moment the participant pressed the button on the computer keyboard. Main findings indicate that processing is facilitated by macro-structure (topicality) in conjunction with micro-structure (morpho-syntax) in pronoun interpretation. It is speculated that global processing alone may not be able to determine which potential antecedent is to be focused unless aided by lexical information. It is argued that the results largely support the resonance-based model, but not the minimalist hypothesis.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.173-183
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• 2003

Morphological analysis is the most widely used method for extracting nouns from Korean texts. For every Eojeol, in order to extract nouns from it, a morphological analyzer performs frequent dictionary lookup and applies many morphonological rules, therefore it requires many operations. Moreover, a morphological analyzer generates all the possible morphological interpretations (sequences of morphemes) of a given Eojeol, which may by unnecessary from the noun extraction`s point of view. To reduce unnecessary computation of morphological analysis from the noun extraction`s point of view, this paper proposes a method for Korean noun extraction considering noun occurrence characteristics. Noun patterns denote conditions on which nouns are included in an Eojeol or not, which are positive cues or negative cues, respectively. When using the exclusive information as the negative cues, it is possible to reduce the search space of morphological analysis by ignoring Eojeols not including nouns. Post-noun syllable sequences(PNSS) as the positive cues can simply extract nouns by checking the part of the Eojeol preceding the PNSS and can guess unknown nouns. In addition, morphonological information is used instead of many morphonological rules in order to recover the lexical form from its altered surface form. Experimental results show that the proposed method can speed up without losing accuracy compared with other systems based on morphological analysis.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.193-205
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• 2021

The AI speaker market is growing steadily. However, the satisfaction of actual users is only 42%. Therefore, in this paper, we collected reviews on Amazon Echo Dot 3rd and 4th generation models to analyze what hinders the user experience through the topic changes and emotional changes of each generation of AI speakers. By using topic modeling analysis techniques, we found changes in topics and topics that make up reviews for each generation, and examined how user sentiment on topics changed according to generation through deep learning-based sentiment analysis. As a result of topic modeling, five topics were derived for each generation. In the case of the 3rd generation, the topic representing general features of the speaker acted as a positive factor for the product, while user convenience features acted as negative factor. Conversely, in the 4^th generation, general features were negatively, and convenience features were positively derived. This analysis is significant in that it can present analysis results that take into account not only lexical features but also contextual features of the entire sentence in terms of methodology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.224-231
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• 2019

The current paper was conducted to unravel the influence of affect induction as a background emotion in the process of cognitive task to judge the degree of sequence in groups with or without anxiety symptoms. Four types of affect induction and two sequential task types were used as within-subject variables, and two types of college students groups classified under the Beck Anxiety Inventory (BAI) as a between-subject variable were selected to determine reaction times involving sequential judgment among the lexical relevance information. DmDx5 was used to present a series of stimuli and elicit a response from subjects. Repeated measured ANOVA analyses revealed that reaction times and error rates were significantly larger with anxiety participants compared to the normal group regardless of affect and task types. Within-subject variable effects found that specific affect type (sorrow condition) and number-related task type showed a more rapid response compared to other affect types and magnitude-related task type, respectively. In sum, these findings confirmed the difference in tendency with reaction time and error rates that varied as a function of accompanying affect types as well as anxiety level and task types suggesting the that underlying background affect plays a major role in processing affect-cognitive association tasks.

• Korean Journal of Cognitive Science
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• v.13 no.1
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• pp.23-40
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• 2002

This study is to examine brain regions that are involved in picture encoding in normal adults using fMRI methods. In Scan 1, the picture encoding was studied during a semantic categorization task in comparison with word. In Scan 2 task type effects were studied both during a picture naming task and during a semantic categorization task with pictures. Subjects were asked to make decision either by pressing a mouse button (Scan 1) or by responding subvocally (naming or saying yes/no) (Scan 2). Regardless of stimulus type, left prefrontal, bilateral occipital, and parietal activations were observed during semantic processing in comparison with fixation baseline. Processing of word stimulus relative to picture resulted in activations in prefrontal and parieto-temporal regions in the left side while that of picture stimulus relative to word resultd in activations in bilateral extrastriatal visual cortices and parahippocampal regions. In spite of the same task demands, stimulus-specific information processings were involved and mediated by different neural substrates; the word encoding was associated with more semantic/lexical processings than pictures and the picture processing associated with more perceptual and novelty related information processings than word. Activations of dorsal part of inferior prefrontal region, i.e., Broca's areas were found both during the picture naming and during the semantic tasks subvocally performed Especially, during the picture naming task, greater occipital activations were found bilaterally relative to the semantic categorization task. indicating a possibility that greater and higher visual processing was involved in retrieving the name referred by picture stimuli.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.153-172
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• 2021

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.127-137
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• 2021

This research studies the perception types of 119 rescue workers among emergency rescue department students, and was carried out to identify the types of perception of 119 rescue workers among firefighters and to prepare basic data to find out the characteristics of each type. As a result of analysis on the Q sample consisting of 27 statements by executing the Q UANL program on a total of 54 students from the Emergency rescue department, it is confirmed that there were 3 types, which accounted for 45% of the total variable. When looking at the explanatory power per type, it turned out: 32% for Type I; 6.7% for Type II; and 5.8% for Type III. Each type was named as follows: our Superman for Type I ; suffering hero for Type II ; and rescue expert for Type III. Overall, there were 119 rescue workers as follows : rescue workers in lexical meaning; and 119 rescue workers who were in difficult situations suffering from post-traumatic stress disorder and needed to be covered and protected by citizens. In addition, there was a perception of 119 rescue workers who were recognized as a specialist and carry out his/her lifesaving duties without a single mistake. Therefore, in order for 119 rescue workers to be recognized as a specialized field of rescue, a program in which 119 rescue workers can share various training and experiences must be provided and researched.