• Journal of Digital Convergence
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• v.14 no.12
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• pp.141-153
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• 2016

The purpose of this study is to predict future of wage-peak system by using text mining, futures wheel and polarity voting (+, -) techniques after reviewing a variety of documents. For this study, we collected articles, news articles, SNS(Twitter, Blog), research report documents. Above all, we extracted keywords for main subject words by utilizing text mining techniques. Next, we drew a final conclusion about future of wage-peak system by using futures wheel and polarity voting techniques. The result showed that future of wage peak system is positive. Two of five main topics were negatively predicted (favor/oppose of wage-peak system, solving task of wage-peak system), however, three of five main topics were positively predicted (background of wage-peak system, purpose/reason of wage-peak system, alternative wage-peak system). Therefore, because three of the five main topics were positively predicted, the future for wage-peak system is positive.

• Geophysics and Geophysical Exploration
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• v.5 no.3
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• pp.199-205
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• 2002

In the resistivity method, the potential difference between two grounded electrodes is measured and this can be positive or negative. The apparent resistivity and the potential difference have the same polarity. Since the electric field is the gradient of the potential, the polarity of the potential difference depends on the direction of the electric field. If the direction of the vector connecting two grounded electrodes is the same to that of the electric field, the measured potential difference and the apparent resistivity become positive. If the opposite is the case, they become negative. In general, the primary electric field and the vector connecting two potential electrodes have the same direction in a surface resistivity method. In this case, the measured potential difference is always positive because the primary electric field is greater than the secondary field. Therefore, the apparent resistivity is always positive if noise is free and topography is flat. The secondary field component, however, can be greater than the primary field component along the vector connecting two potential electrodes in the cross-hole resistivity method. Furthermore, if the secondary electric field and the vector connecting two potential electrodes have an opposite direction, the apparent resistivity become negative. Consequently, the apparent resistivity may be negative in the region where the primary electric field component along the vector connecting two potential electrodes is very small.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

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 repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new 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.

• Korean Journal of Materials Research
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• v.11 no.7
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• pp.599-602
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• 2001

In this study, we fabricated chemically polymerized PPy and PANi films with different selectivity by controlling dedoping time. And the sensing properties and mechanism of VOCs adsorption to conducting polymers were investigated. Thin sensor had higher sensitivity compared to thick one, and dedoped sensor for 1-minute highest sensitivity. Upon gas absorption, polypyrrole exhibited positive sensitivity while polyaniline had negative sensitivity. PPy film show hydrophilic property and PANi film show hydrophobic property. After the gas absorption, the sensitivity increased as a function of polarity of absorbed molecules. These behaviors are due to the polar molecules absorbed with the movable polaron or free carrier, and then it interrupt or generate the movement of polaron and carrier, and then it changes the conductivity of polymer. We found that conducting polymer sensors are very sensitive to the difference in polarity of gas molecules.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.165-170
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• 2016

The characteristic and application of a new duality structure in the $NCV-{\mid}v_1$ > library is studied in this paper. All unitary operations on arbitrarily many qudit's n can be expressed as composition of one- and two-qudit $NCV-{\mid}v_1$ > libraries if their state vectors are eigenvectors. This research provides an extended realization from Barenco's many bits n operator(U(2n)) which is applicable to only all positive polarity statevectors to whole polarity ones. At the control gate synthesis of a unitary operator, such an enhanced expansion is possible due to their symmetric duality property in the case of using both $NCV-{\mid}v_1$ > and $NCV^{\dag}-{\mid}v_1$ > libraries which make the AND predominantly dependent cascade synthesis possible.

• Molecules and Cells
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• v.38 no.3
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• pp.243-250
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• 2015

Patterning of the polar axis during the early leaf developmental stage is established by cell-to-cell communication between the shoot apical meristem (SAM) and the leaf primordia. In a previous study, we showed that the DRL1 gene, which encodes a homolog of the Elongator-associated protein KTI12 of yeast, acts as a positive regulator of adaxial leaf patterning and shoot meristem activity. To determine the evolutionally conserved functions of DRL1, we performed a comparison of the deduced amino acid sequence of DRL1 and its yeast homolog, KTI12, and found that while overall homology was low, well-conserved domains were presented. DRL1 contained two conserved plant-specific domains. Expression of the DRL1 gene in a yeast KTI12-deficient yeast mutant suppressed the growth retardation phenotype, but did not rescue the caffeine sensitivity, indicating that the role of Arabidopsis Elongator-associated protein is partially conserved with yeast KTI12, but may have changed between yeast and plants in response to caffeine during the course of evolution. In addition, elevated expression of DRL1 gene triggered zymocin sensitivity, while overexpression of KTI12 maintained zymocin resistance, indicating that the function of Arabidopsis DRL1 may not overlap with yeast KTI12 with regards to toxin sensitivity. In this study, expression analysis showed that class-I KNOX genes were downregulated in the shoot apex, and that YAB and KAN were upregulated in leaves of the Arabidopsis drl1- 101 mutant. Our results provide insight into the communication network between the SAM and leaf primordia required for the establishment of leaf polarity by mediating histone acetylation or through other mechanisms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.243-253
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• 2012

An opinion mining technique which was developed from document classification in area of data mining now becomes a common interest in domestic as well as international industries. The core of opinion mining is to decide precisely whether an opinion document is a positive or negative one. Although many related approaches have been previously proposed, a classification accuracy was not satisfiable enough to applying them in practical applications. A opinion documents written in Korean are not easy to determine a polarity automatically because they often include various and ungrammatical words in expressing subjective opinions. Proposed in this paper is a new approach of classification of opinion documents, which considers only a frequency of word patterns and excludes the grammatical factors as much as possible. In proposed method, we express a document into a bag of words and then apply a learning algorithm using a frequency of word patterns, and finally decide the polarity of the document using a score function. Additionally, we also present the experiment results for evaluating the accuracy of the proposed method.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.13 no.1
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• pp.85-93
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• 2000

In this paper, the predischarge propagation processes of SF\ulcorner gas stressed by impulse voltages under nonuniform electric field perturbed by a needle protrusion are described. The statistical and formative time-lags and the time interval between leader pulses were investigated on the basis of the predischarge current measured in the gas pressure range of 0.1~0.5 MPa. The predischarge current is closely related to the waveform, amplitude and polarity of applied votages, the gas pressure and the gap geometry. Both the positive and negative predischarge processes in nonuniform electric field develop in a regime of stepwise leader propagation leading to electrical breakdown. The mean of the time interval between leader pulses gives about a factor of 10 higher for the negative than for the positive leader current puls-es. According as the gas pressure increases, the statistical time-lag was almost unchangeable, but the formative time-lag was gradually decreased.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.329-336
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• 2010

The paper describes the electrical and optical properties of underwater discharges in highly inhomogeneous electric fields caused by 1.2/50 ${\mu}s$ impulse voltages as functions of the polarity and amplitude of the applied voltage, and various water conductivities. The electric fields are formed by a point-to-plane electrode system. The formation of air bubbles is associated with a thermal process of the water located at the tip of the needle electrode, and streamer coronas can be initiated in the air bubbles and propagated through the test gap with stepped leaders. The fastest streamer channel experiences the final jump across the test gap. The negative streamer channels not only have more branches but are also more widely spread out than the positive streamer channels. The propagation velocity of the positive streamer is much faster than that of the negative one and, in fact, both these velocities are independent of the water conductivity; in addition the time-lag to breakdown is insensitive to water conductivity. The higher the water conductivity the larger the pre-breakdown energy, therefore, the ionic currents do not contribute to the initiation and propagation of the underwater discharges in the test conditions considered.