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Sentiment expression in microblog posts can be affected by user's personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user's language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem. Results on English and Chinese microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.

This article considers the problem of estimating the cell frequencies in a contingency table under inequality constraints. Algorithms are proposed for cell frequency estimation via minimizing the Kullback–Leibler distance subject to inequality constraints. The proposed algorithms are shown to be simple, easy to be used, fast, and reliable. Theorems are derived to guarantee the convergence of the algorithms. Applications and extensions of the algorithms are provided for more general problems than contingency table. The R programs that implement the proposed algorithms are presented in Appendix B.

The Chinese economy is changing its development model from invest/export-led growth to consumption/growth driven by domestic demand. In the recent domestic-oriented growth phases of China's opening-up policy, what role is expected to play? Not only is this a way to acquire foreign exchange and technology, but it is also an economic powerhouse for China that strengthens its position in global governance. General Secretary Xi Jinping's proposed the "Belt and Road" initiative in 2013, which is considered an excellent strategy for China's new round of opening-up policy. This research paper aims to bring the BRI inside China's open-up policy and focuses on its increasingly important role in controlling major global economic regimes. The study also shows the importance of establishing systems of mutual aid/funding for the countries participating in BRI. The first section of the paper looks at China's reactions to major economic regimes. Section two reflects the design and the implementation of the opening-up strategy for China from the BRI perspective. Section three of the research addresses Chinese development aid/funding in the BRI and its relation to foreign regimes. The fourth part addresses the BRI's trade relationship with the participating countries, using the gravity model from a global commercial perspective. The study concluded that cross-border financial cooperation between government and business participants is vital for the development of a successful investment and funding system for the BRI. It reiterated the importance of using foreign and regional financial centers to develop a regional structure within the BRI's investment and financing system.

This qualitative study aimed to explore the reasons why China has become a global destination for international students. Recently, the enrollment of International Students has increased at Chinese Universities. The study aims to understand why such International Students from Bangladesh, India, and Pakistan choose to study in China for tertiary education rather than studying in any US or European Universities. To conclude the study, a mixed-method research methodology was used by using focus group discussions and descriptive statistics from two different surveys. The research establishes that the maximum of the international students have chosen China as their study destination is because of safety and security at the campus, proximity with their homeland, low cost of education and accommodation, and a strong perception of prospective job opportunities upon completion of their degrees. The study suggested that Chinese Universities can initiate different plans/ schemes in attracting international students from different countries of the world.

In recent years there has been a growing interest in text quantification, a supervised learning task where the goal is to accurately estimate, in an unlabelled set of items, the prevalence (or "relative frequency") of each class c in a predefined set C. Text quantification has several applications, and is a dominant concern in fields such as market research, the social sciences, political science, and epidemiology. In this paper we tackle, for the first time, the problem of ordinal text quantification, defined as the task of performing text quantification when a total order is defined on the set of classes; estimating the prevalence of "five stars" reviews in a set of reviews of a given product, and monitoring this prevalence across time, is an example application. We present OQT, a novel tree-based OQ algorithm, and discuss experimental results obtained on a dataset of tweets classified according to sentiment strength.