• The Southeast Asian review
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• v.28 no.1
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• pp.21-51
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• 2018

This article takes several approaches in explaining recent developments in Vietnam. First, it draws upon an array of sources that idealize Vietnam's embrace of capitalism and integration into the global market in order to sketch out its economy's progress in 2017. Second, it observes, evaluates, and diagnoses recent changes in the Vietnamese economy in the medium to long term by incorporating conflicting perspectives on Vietnam's performance as a capitalist country. Third, this article traces the power shifts that have risen from internal struggles in the Communist Party over political and social issues. Fourth, it elaborates on the aforementioned impact that foreign relations have had on socio-political developments in Vietnam, as well as the government's response. In so doing, it also attempts to evaluate, however briefly, the significance of the 25th anniversary of South Korea-Vietnam relations. Finally, it examines the public's reaction to the post-reform transitions in light of recent sociocultural changes. 2017 was a memorable year for Vietnam: a continuous march toward capitalism; the resulting expansion of the Vietnamese people's demands; political controversies and government control; the looming instability of United States-China relations and various attempts to address the situation. These events will inevitably replicate themselves in the future as the ostensibly socialist Vietnam adopts a capitalist model. The problem is that it is unclear whether these experiences will continue with the consent of the people of socialist Vietnam or engender resistance. It is difficult to achieve meaningful consent in the status quo of worsening inequalities, widespread corruption, monopoly on power, and sustained use of unskilled low-wage workers. In other words, when concerns such as welfare, public health, and the environment are set aside in favor of economic development and commercialization as they have been, discontent, rather than consent, will prevail. It is thus important to keep a watchful eye on the viability of the nominal economic growth, surface-level political stability, and strategic responses to foreign relations that took place in 2017.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.