• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Korean Journal of Veterinary Research
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• v.9 no.1
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• pp.23-62
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• 1969

Throughout the studies the following experimental results were obtained and are summarized: 1. Multiplication of agents in primary cell cultures of both GF classical and CR-64 acute strain of Marek's disease infected chicken kidneys was accompanied by the formation of distinct transformed cell foci. This characteristic nature of cell transformation was passaged regularly by addition of dispersed cell from infected cultures to normal chicken kidney cell cultures, and also transferred was the nature of cell transformation to normal chick-embryo liver and neuroglial cell cultures. No cytopathic changes were noticed in inoculated chick-embryo fibroblast cultures. 2. The same cytopathic effects were noticed in normal kidney cell monolayers after the inoculation of whole blood and huffy coat cells derived from both forms of Marek's disease infected chickens. In these cases, however, the number of transformed cell foci appearing was far less than that of uninoculated monolayers prepared directly from the kidneys of Marek's disease infected chickens. 3. The change in cell culture IS regarded as a specific cell transformation focus induced by an oncogenic virus rather than it plaque in slowly progressing cytopathic effect by non-oncogenic viruses, and it is quite similar to RSV focus in chick-embryo fibroblasts in many respects. 4. The infective agent (cell transformable) were extremely cell-associated and could not be separated in an infective state from cells under the experimental conditions. 5. The focus assay of these agents was valid as shown by the high degree of linear correlation (r=0.97 and 0.99) between the relative infected cell concentration (in inoculum) and the transformed cell foci counted. 6. No differences were observed between the GF classical strain and the CR-64 acute strain of Marek's disease as far as cell culture behavior. 7. Characterization of the isolates by physical and chemical treatments, development of internuclear inclusions in Infected cells, and nucleic acid typing by differential stainings and cytochemical treatments indicated that the natures of these cell transformation agents closely resemble to those described fer the group B herpes viruses. 8. Susceptible chicks inoculated with infected kidney tissue culture cells developed specific lesions of Marek's disease, and in a case of prolonged observation after inoculation (5 weeks) the birds developed clinical symptoms and gross lesions of Marek's disease. Kidney cell cultures prepared from those inoculated birds and sacrificed showed a superior recovery of cell transformation property by formation of distinct foci. 9. Electron microscopic study of infected kidney culture cells (GF agent) by negative staining technique revealed virus particles furnishing the properties of herpes viruses. The particle was measured about $100m{\mu}$ and, so far, no herpes virus envelop has been seen from these preparations. 10. No relationship of both isolates to avian leukosis/sarcoma group viruses and PPLO was observed.