DIGITAL LIBRARY ARCHIVE
HOME > DIGITAL LIBRARY ARCHIVE
< Previous   List   Next >  
Predicting the Direction of the Stock Index byUsing a Domain-Specific Sentiment Dictionary
Full-text Download
Eunji Yu (Graduate School of Business IT, Kookmin University)
Yoosin Kim (Graduate School of Business IT, Kookmin University)
Namgyu Kim (Graduate School of Business IT, Kookmin University)
Seung Ryul Jeong (Graduate School of Business IT, Kookmin University)
Vol. 19, No. 1, Page: 95 ~ 110
10.13088/jiis.2013.19.1.095
Keywords
Big Data Analysis, Opinion Mining, Sentiment Dictionary Construction, Text Mining
Abstract
Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools.
Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants’ opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches.
One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news reports. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of news content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices.
So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature.
The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision?support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day’s stock index. In addition, we applied a domain?specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative.
For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by “M” and “E” media between July 2011 and September 2011.
Show/Hide Detailed Information in Korean
주가지수 방향성 예측을 위한 주제지향 감성사전 구축 방안
유은지 (국민대학교 Business IT 전문대학원)
김유신 (국민대학교 Business IT 전문대학원)
김남규 (국민대학교 Business IT 전문대학원)
정승렬 (국민대학교 Business IT 전문대학원)
Abstract
최근 다양한 소셜미디어를 통해 생성되는 비정형 데이터의 양은 빠른 속도로 증가하고 있으며, 이를 저장, 가공, 분석하기 위한 도구의 개발도 이에 맞추어 활발하게 이루어지고 있다. 이러한 환경에서 다양한 분석 도구를 통해 텍스트 데이터를 분석함으로써, 기존의 정형 데이터 분석을 통해 해결하지 못했던 이슈들을 해결하기 위한 많은 시도가 이루어지고 있다. 특히 트위터나 페이스북을 통해 실시간에 근접하게 생산되는 글들과 수많은 인터넷 사이트에 게시되는 다양한 주제의 글들은, 방대한 양의 텍스트 분석을 통해 많은 사람들의 의견을 추출하고 이를 통해 향후 수익 창출에 기여할 수 있는 새로운 통찰을 발굴하기 위한 움직임에 동기를 부여하고 있다. 뉴스 데이터에 대한 오피니언 마이닝을 통해 주가지수 등락 예측 모델을 제안한 최근의 연구는 이러한 시도의 대표적 예라고 할 수 있다. 우리가 여러 매체를 통해 매일 접하는 뉴스 역시 대표적인 비정형 데이터 중의 하나이다. 이러한 비정형 텍스트 데이터를 분석하는 오피니언 마이닝 또는 감성 분석은 제품, 서비스, 조직, 이슈, 그리고 이들의 여러 속성에 대한 사람들의 의견, 감성, 평가, 태도, 감정 등을 분석하는 일련의 과정을 의미한다. 이러한 오피니언 마이닝을 다루는 많은 연구는, 각 어휘별로 긍정/부정의 극성을 규정해 놓은 감성사전을 사용하며, 한 문장 또는 문서에 나타난 어휘들의 극성 분포에 따라 해당 문장 또는 문서의 극성을 산출하는 방식을 채택한다. 하지만 특정 어휘의 극성은 한 가지로 고유하게 정해져 있지 않으며, 분석의 목적에 따라 그 극성이 상이하게 나타날 수도 있다. 본 연구는 특정 어휘의 극성은 한 가지로 고유하게 정해져 있지 않으며, 분석의 목적에 따라 그 극성이 상이하게 나타날 수도 있다는 인식에서 출발한다. 동일한 어휘의 극성이 해석하는 사람의 입장에 따라 또는 분석 목적에 따라 서로 상이하게 해석되는 현상은 지금까지 다루어지지 않은 어려운 이슈로 알려져 있다. 구체적으로는 주가지수의 상승이라는 한정된 주제에 대해 각 관련 어휘가 갖는 극성을 판별하여 주가지수 상승 예측을 위한 감성사전을 구축하고, 이를 기반으로 한 뉴스 분석을 통해 주가지수의 상승을 예측한 결과를 보이고자 한다.
Cite this article
JIIS Style
Yu, E., Y. Kim, N. Kim, and S. R. Jeong, "Predicting the Direction of the Stock Index byUsing a Domain-Specific Sentiment Dictionary", Journal of Intelligence and Information Systems, Vol. 19, No. 1 (2013), 95~110.

IEEE Style
Eunji Yu, Yoosin Kim, Namgyu Kim, and Seung Ryul Jeong, "Predicting the Direction of the Stock Index byUsing a Domain-Specific Sentiment Dictionary", Journal of Intelligence and Information Systems, vol. 19, no. 1, pp. 95~110, 2013.

ACM Style
Yu, E., Kim, Y., Kim, N., and Jeong, S. R., 2013. Predicting the Direction of the Stock Index byUsing a Domain-Specific Sentiment Dictionary. Journal of Intelligence and Information Systems. 19, 1, 95--110.
Export Formats : BiBTeX, EndNote

Warning: include(/home/hosting_users/ev_jiisonline/www/admin/archive/advancedSearch.php) [function.include]: failed to open stream: No such file or directory in /home/hosting_users/ev_jiisonline/www/archive/detail.php on line 429

Warning: include() [function.include]: Failed opening '/home/hosting_users/ev_jiisonline/www/admin/archive/advancedSearch.php' for inclusion (include_path='.:/usr/local/php/lib/php') in /home/hosting_users/ev_jiisonline/www/archive/detail.php on line 429
@article{Yu:JIIS:2013:522,
author = {Yu, Eunji and Kim, Yoosin and Kim, Namgyu and Jeong, Seung Ryul},
title = {Predicting the Direction of the Stock Index byUsing a Domain-Specific Sentiment Dictionary},
journal = {Journal of Intelligence and Information Systems},
issue_date = {March 2013},
volume = {19},
number = {1},
month = Mar,
year = {2013},
issn = {2288-4866},
pages = {95--110},
url = {http://dx.doi.org/10.13088/jiis.2013.19.1.095 },
doi = {10.13088/jiis.2013.19.1.095},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Big Data Analysis, Opinion Mining, Sentiment Dictionary Construction and Text Mining },
}
%0 Journal Article
%1 522
%A Eunji Yu
%A Yoosin Kim
%A Namgyu Kim
%A Seung Ryul Jeong
%T Predicting the Direction of the Stock Index byUsing a Domain-Specific Sentiment Dictionary
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 19
%N 1
%P 95-110
%D 2013
%R 10.13088/jiis.2013.19.1.095
%I Korea Intelligent Information System Society