Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald’s (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

Jaemulbo (Caiwupu) is a dictionary rich in words, and has the nature of an encyclopedia. Giving a brief description of the collection and interpretation of the terms of plants and animals and observing the acceptance of the Chinese characters and words in the Korean Peninsula, this article singles out four myeongmul (meibutsu) words for textual research in order to observe the characteristics of vocabulary spread to ancient Korea. It has four main objectives. The first is to support the authoritative theory. The second is to ignore character and word relationships that lead to errors although scholars know that glyphs can be used universally. The third is to be cautious not to follow them blindly. The last is to influence the culture of the nation’s famous objects. It is expected that this research can make a significant contribution to the future studies on the spread of Chinese characters.

This paper examined factors affecting Korean EFL learners’ word association types in their L2 mental lexicon. Specifically, vocabulary size of the learners was examined to see if it had any significant relationship with word association types. To this end, experiment procedures that included vocabulary size test and the lexical decision task as well as the word association task were conducted on 40 Korean EFL learners. Reaction time and accuracy of responding to word associations in the lexical decision task were measured. Subsequently, a correlation analysis was conducted with their vocabulary size. Additionally, learners’ word association types were analyzed based on the results of word association task. The results showed that Korean EFL learners’ vocabulary size had significant correlations with their accuracy in identifying syntagmatic and paradigmatic associations, but not in the phonological association. However, their accuracy was not correlated with reaction times. The results indicate that L2 learners’ mental lexicon is partially dependent on their vocabulary size of the target language, and it is variable depending on word association types.

Landscape urbanism suggests direction on the method of performing landscape through a practical strategy to deal with urban problems, by using landscape as a medium. However, even though such a practical topic has been advocated, failure to resolve the difference between theory and practice has been criticized. To identify the vagueness of the concept of landscape urbanism, this study aims to identify the differential properties of texts that appear in discourse. It analyzes the discourse on the word “landscape” and looks at the relation between texts and contexts that form the discourse from a semantic approach. The texts have been extracted from the discourse of Corner and Waldheim where their meaning and signification are derived through the contextualization and systematization of the word “landscape”. In landscape urbanism, the word “landscape” has a conceptual representation of the theory, and the textual context contained in “landscape” is complex and multi-layered. The major texts related to the word “landscape” have semantic relations that have denotative and connotative meaning. The affiliation with the subject of landscape relates to the denotative meaning, and the texts related to the properties of landscape have a connotative meaning. The lexicon of the word “landscape” is a complex system, which forms its signification through emergence and self-organization. Meanwhile, the word “landscape” has the individuality of texts in complicated relations, including inevitable vagueness and contingency, which limits the study in applying and evaluating the analysis objectively.

Over the past decade, the magnitude and variety of modelling work in the realm of veterinary services has shown a significant increase. Accordingly, the need for enhancement of communication and cooperation among modellers (those who develop models), users (those who run models), policy makers (those who use model outputs in decision making on control policy) and coordinators (those who connect modellers with policy makers) has also shown a notable increase. In this paper, terms used in epidemiological models are listed alphabetically and explained. It is expected that development of this lexicon will help to boost communication and collaboration among those concerned with modelling work and its utilization.

감성 분석은 글을 통해 작성자의 주관적인 생각이나 느낌을 분석하는 방법으로 효과적인 감성 분석을 위해서는 감성 단어 극성 사전 구축이 필수적이다. 본 논문은 효율적인 한국어 극성 사전 구축을 위해 우리가 개발한 크라우드소싱 기반 게임을 소개한다. 먼저, 크롤러를 이용해 인터넷 커 뮤니티에서 말뭉치들을 수집했고, Twitter 형태소를 이용해 수집한 말뭉치를 형태소별로 분류하고 단어화했다. 이 단어들은 모바일 플랫폼 기반 태깅 게임 형태로 제공되어 게임플레이를 통해 플레 이어들이 자발적으로 단어들의 극성을 선택하고 결과가 데이터 베이스에 축적되도록 게임이 설계 되었다. 현재까지 약 1200여개의 단어들의 극성을 태깅하였으며, 향후 좀 더 많은 감성 단어 데이 터들을 축적함으로써 특히 게임 도메인에서 한국어 감성 분석 연구에 기여할 것으로 기대한다.