• Journal of agriculture & life science
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• v.45 no.5
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• pp.81-89
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• 2011

This study was carried out to investigate changes in physicochemical and sensory properties of cooked sausages replaced sodium chloride (NaCl) to potassium chloride (KCl) or magnesium chloride ($MgCl_2$) during storage for 30 days under $4^{\circ}C$. All sausages were prepared with different combination of salts as follow; CTL (1.5% NaCl), KCL (0.9% NaCl+0.6% KCl), MCL (0.9% NaCl+0.6% $MgCl_2$), KML (0.9% NaCl+0.3% KCl+0.3% $MgCl_2$) and PST (1.5% PanSalt). Among sausages moisture content in KML was the highest (p<0.05). Lightness and redness in CTL were lower than those of other treatments, but MCL and KML containing $MgCl_2$ showed higher CIE $L^*$ and $a^*$ values than CTL. The pH in CTL was the highest during storage, however, no significant difference was determined between two treatments, MCL and KML (p>0.05). Crude fat content and water holding capacity (WHC), hardness and cohesiveness of MCL sausages were higher than those of CTL. In sensory characteristics of cooked sausages, saltness in MCL was the lowest during 10 and 20 days of storage (p<0.05). Yellowness in PST was lower than other treatmeants. Gumminess and chewiness of texture property of sausages from MCL and KML were higher than CTL. The results indicate that the replacement of NaCl by KCl improved texture, but meat color was not improved as expected. In contrast, the replacement of NaCl by $MgCl_2$ enhanced color, texture and WHC, whereas partial replacement of NaCl by $MgCl_2$ must reduce bitter taste as compared to sausages manufactured with a NaCl only. Therefore, $MgCl_2$ may be a salt replacing NaCl in cooked pork sausages.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.