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This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification.

This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts.

This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy.

This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.

The research aims at scrutinising food safety as a global concept and problem that has numerous cross-cultural aspects reflecting the diversity of consumption patterns and the culturally differing role of the consumer as well as mirroring the heterogeneity of socio-economic environment.

In this paper, the role of consumer in food safety-related academic literature is investigated in seven languages (countries) including American English, French, German, Hungarian, Italian, Mandarin Chinese and Russian from a multidisciplinary, cross-cultural perspective.

With the aid of seven linguistic corpora built from the above mentioned languages, the research reveals noteworthy differences in the consumer-focused approach.

The study could have benefited from the inclusion of further languages (i.e. Portuguese, Spanish, Hindi etc.), the authors' lack of reliable language skills outside of the covered domain had to be taken into account. Further to that, the analysis conducted is based on a static observation, while food safety-related consumer science is developing quickly. Therefore, a dynamic analysis of consumer roles would most certainly yield in further salient outcome.

Food safety can be regarded in many ways–this is reflected in different national legislations, dissimilar country-level risk communication patterns as well as different perception of basic notions of food safety. It has not yet been extensively analysed, however, how different languages use the notion of food safety or consumer, which activities and which characteristics are most connected to these notions, and how food safety-related topics and the focus of scientific discourse in different languages differ from each other.

Practical implications of the research results also include preparatory activities for food safety risk communication campaigns. In this field, the cultural aspects of food safety are as important as scientific risk assessment. The tools presented in this paper help a quick and comprehensive analysis of linguistic corpora, which could be used either in academic or general literature resources, even press releases. The results also call attention to the culture-driven perspectives of food safety; these new insights can be applied by researchers to review food safety literature more exhaustively considering the cultural context. Future elaboration of the topic (e.g. by introducing a time factor that would enable a dynamic analysis) can further enhance the utility value of similar studies.

The novelty of the article lies in the unique application of corpus linguistic methods with the aim of investigating the area, the trends and phenomena of food safety-related science. This study combines the achievements of food safety-related consumer science with corpus linguistic methods.

To study how the collocational networks method could be used to analyze textual contents in listed companies' annual reports written in Chinese, in an attempt to identify hidden facts that are not released in a listed company's financial statements.

This research extended the collocation network analysis method from English textual contents to Chinese textual contents. The extended collocation network method was used to analyze an Information Technology company, Clever, listed in Shanghai stock exchange of China.

Using the extended collocational networks method, some hidden facts about a Chinese listed company's financial status could be identified, which were not reported in company's officially released financial statements.

The extended collocation network method may supplement the commonly practiced fundamental financial analysis method in helping investors have a better understanding about the financial soundness of listed companies. This is especially important to investors in stock markets of some developing countries, including China. In addition, this method may help regulators of stock market, especially in developing countries, to identify possible loopholes of existing financial regulations as well as some inappropriate practices of some listed companies in disclosing misleading or incorrect financial data in their financial statements.

The first study using the collocational networks method to analyze annual reports of Chinese corporations listed in Shanghai stock exchange, a newly established stock market.

After over 30 years’ reform and opening-up, China as the second largest economy is now facing the most essential transformation of management philosophy and the biggest challenging issue of business sustainable development, with people’s increasing worry of the deterioration of environmental pollution, food security and human health. It can be said that what China needs urgently today is business ethical value and long-term sustainable development concept, rather than rapidly growing GDP. The purpose of this paper is to assess how the term “sustainable development” is constructed and valued in the sustainability reports or corporate social responsibility (CSR) reports of Chinese corporations, so as to interpret these Chinese firms’ conception of sustainable development in their real business practices.

A corpus of sustainability reports collected from 30 Chinese corporations totaling 247,311 tokens is first of all compiled to realize the objective of study. Then the authors use the AntConc, a corpus analysis toolkit, to generate word lists, key-word-in-context concordances and collocation lists, as well as calculating statistical significance measures for collocates, of which the mutual information (MI) score 3 is most relevant to the paper’s purposes. Based on the key-word-in-context concordance and collocation list, the authors can find what context “sustainable development” usually appears in sustainability reports, thus inferring Chinese corporations’ conception of sustainable development.

The result indicates that Chinese corporations use the rhetoric of weak sustainability, indicating that sustainable development is compatible with further economic growth, which means that Chinese corporations in current China, strongly promoting the concept of new normal economy, still put economic growth as a dominant goal, on which other dimensions of sustainability like environmental protection depend.

The data gleaned in current corpus are limited to the sustainability reports in 2014 thus the study provides no hints as to diachronic trends. However, this study increases our understanding of how Chinese corporations attach value to sustainable development from the view of corpus analysis.

Different from traditional discourse analysis, which usually carries out qualitative analysis to analyze how a word or phrase is constructed in a small number of texts, the authors’ study innovatively introduces the method of corpus analysis to explore how Chinese corporations construct “sustainable development” in their sustainability reports. Thus, the number of texts analyzed is larger in the authors’ study and their findings are more representative and convincing. The authors create a more qualitative understanding of what the reports are actually saying on their reports and prove that corpus methods can bring new application to the discourse analysis of the biggest challenging issue of China’s future economic growth, suggesting a potential novel way to work out the meaning and implication of sustainable development in Chinese real business world.

The purpose of this study is to apply a corpus-assisted analysis of keywords and their collocations in the US presidential discourse from Clinton to Trump to discover the meanings of these words and the collocates they have. Keywords are salient words in a corpus whose frequency is unusually high (positive keywords) or low (negative keywords) in comparison with a reference corpus. Collocation is the co-occurrence of words.

To achieve this purpose, the investigation of keywords and collocations is generated by AntConc, a corpus processing software.

This analysis leads to shed light on the similarities and/or differences amongst the past four American presidents concerning their key topics. Keyword analysis through keyness makes it evident that Clinton and Obama, being Democrats, demonstrate a clear tendency to improve Americans’ life inside their social sphere. Obama surpasses Clinton as regard foreign affairs. Clinton and Obama’s infrequent subjects have to do with terrorism and immigration. This complies with their condensed focus on social and economic improvements. Bush, a republican, concentrates only on external issues. This is proven by his keywords signifying war against terrorism. Bush’s negative use of words marking cooperative actions conforms to his positive use of words indicating external war. Trump’s positive keywords are about exaggerated descriptions without a defined target. He also shows an unusual frequency in referring to his name and position. His words used with negative keyness refer to reforming programs and external issues. Collocations around each top content keyword clarify the word and harmonize with the presidential orientation negotiated by the keywords.

Limitations have to do with the issue of the accurate representation of the samples.

This research is original in its methodology of applying corpus linguistics tools in the analysis of presidential discourses.

This paper, from the perspective of based view of dynamic system, aims to take the family enterprise as a sample to articulate how the speed of institutional change affects the entrepreneur’s spirit collocation of family enterprises and investigate the moderating effects of the scale of enterprises as well.

This paper uses survey database from Chinese research of private enterprise group in 2010 with the ninth national large-scale private entrepreneurs, and the legal source of data comes from research center for Chinese family firm of Sun Yat-Sen University. A total of 4,900 questionnaires are issued, 4,614 are recovered and the total recovery rate is 94.16 per cent. In this paper, STATA12.0 is used for data processing and basic regression testing. To overcome the possible existence of the different variance problem, the authors use the feasible generalized least squares to estimate the model.

The speed of institutional change will lead to the reduction of unproductive activities and the increase of productive activities in the area where the speed of institutional change is slow. Meanwhile, the scale of enterprise can reverse the negative relationship between the speed of institutional change and unproductive activities. The speed of institutional change will lead to the reduction of unproductive activities and the increase of productive activities in the area where the speed of institutional change is fast. Meanwhile, the scale of enterprises can reverse the positive relationship between the speed of institutional change and the unproductive activities.

It can be concluded that because of the difference of the regional market, a positive U-type reflects the relationship between the speed of institutional change and the entrepreneur’s allocation of entrepreneurship in family firms, whereas the scale of enterprises plays a key role of nonlinear regulation. This research has a certain theoretical value and practical significance on the understanding of how family firms make strategic decisions in response to institutional change and it can further enrich the research results of entrepreneurship allocation theory and institutional change theory.

The purpose of this paper is to study the realization of turn‐over‐wok movement for a cooking robot.

First, the authors introduce the cooking robot's movement mechanism and its realization methods and then analyze the movement geometric of the movement mechanism. The authors conduct the force analysis of the material in the wok motion and suppose the moving situation of mass point m when turning over the wok and shaking the wok.

The problem of the cooking robot simulating the special cooking motion made by a chef is solved.

The robot is applicable to being a cooking robot for Chinese dishes.

The paper shows how to optimize the effect of material moving track and satisfies the requirements of wok motion of cooking robot.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

The purpose of this paper is to study the reasons and decision-making processes of heterogeneous firms’ bribery behavior, and how they will affect an aggregate economy’s development and corruption status.

The authors build a dynamic model to study a firm’s joint decision to bribe and invest, and how the decision is determined by its production and infrastructure status. The authors simulate the firm-level decision and development paths, and then build an aggregate economy consisting of heterogeneous firms. The authors then also simulate the development and corruption growth paths of the economy, by calibrating the model according to Chinese manufacturing firms in 2012.

Following the simulation results, the authors conduct counterfactual policy analyses. By comparing between the simulation results of two different counterfactual scenarios, the authors study how a government could control bribing better – as to decrease the number of bribers, and the average amount of the bribery payments. It is found that directly raising the bribery costs works more efficiently in controlling corruption, compared with reducing the benefits received by the bribers. The finding provides insightful policy implications for the government to clear up its economy.

The paper makes a novel and unique contribution to the literature by filling the current theoretical gap. The authors introduce a dynamic firm-level model to interpret firms’ bribery decisions and replicate the aggregate stylized facts. The paper innovatively treats bribery as both discrete and continuous decisions. Given both types of bribery decisions, now the authors can successfully simulate and quantify a firm’s intertemporal status and growth path.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.