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Learning Chinese is unquestionably very important and popular worldwide with the fast economic growth of China. To most foreigners and also local students, one of the major challenges in learning Chinese is to write Chinese characters in correct stroke sequences that are considered as significant in the Chinese culture. However, due to the potentially complicated structures of Chinese characters together with their stroke sequences, there are very few character recognition techniques that can effectively tackle the involved training task in an efficient and flexible manner. The purpose of this paper is to propose an intelligent and flexible e‐learning software based on learning objects to facilitate the learning of writing Chinese characters in correct stroke sequences.

The paper adopts an incremental approach in designing the overall system architecture to emphasize on extendibility of the system. The basic features of the system including the evolution and pronunciation of each Chinese character can be embedded as a part of the learning object metadata to enhance students' understanding of Chinese characters. To demonstrate the feasibility of this proposal, a prototype of the proposed e‐learning software was built on smartphones such that students can learn anytime and anywhere.

From the empirical evaluation of the e‐learning prototype for learning to write correct Chinese characters on mobile devices, it was found that foreign students can learn and practise the writing more effectively anytime and anywhere on their mobile devices after classes. Some initial positive feedback was collected. Furthermore, a more careful and thorough evaluation is planned to be conducted in relevant courses for foreign students in the upcoming Fall semester.

This proposal represents the first attempt to reduce the complexity while increasing the extendibility of the e‐learning software to learn Chinese through learning objects running on smartphones or mobile devices in general. More importantly, it opens up numerous opportunities for further investigations including possible integrations with other existing Chinese e‐learning systems.

In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly. It is not practical to directly migrate achievements obtained in English sentiment analysis to the analysis of Chinese because of the huge difference between the two languages.

In view of the particularity of Chinese text and the requirement of sentiment analysis, a Chinese sentiment analysis model integrating multi-granularity semantic features is proposed in this paper. This model introduces the radical and part-of-speech features based on the character and word features, with the application of bidirectional long short-term memory, attention mechanism and recurrent convolutional neural network.

The comparative experiments showed that the F1 values of this model reaches 88.28 and 84.80 per cent on the man-made dataset and the NLPECC dataset, respectively. Meanwhile, an ablation experiment was conducted to verify the effectiveness of attention mechanism, part of speech, radical, character and word factors in Chinese sentiment analysis. The performance of the proposed model exceeds that of existing models to some extent.

The academic contribution of this paper is as follows: first, in view of the particularity of Chinese texts and the requirement of sentiment analysis, this paper focuses on solving the deficiency problem of Chinese sentiment analysis under the big data context. Second, this paper borrows ideas from multiple interdisciplinary frontier theories and methods, such as information science, linguistics and artificial intelligence, which makes it innovative and comprehensive. Finally, this paper deeply integrates multi-granularity semantic features such as character, word, radical and part of speech, which further complements the theoretical framework and method system of Chinese sentiment analysis.

Sentiment identification of Chinese text faces many challenges, such as requiring complex preprocessing steps, preparing various word dictionaries carefully and dealing with a lot of informal expressions, which lead to high computational complexity.

A method based on Chinese characters instead of words is proposed. This method represents the text into a fixed length vector and introduces the chi-square statistic to measure the categorical sentiment score of a Chinese character. Based on these, the sentiment identification could be accomplished through four main steps.

Experiments on corpus with various themes indicate that the performance of proposed method is a little bit worse than existing Chinese words-based methods on most texts, but with improved performance on short and informal texts. Especially, the computation complexity of the proposed method is far better than words-based methods.

The proposed method exploits the property of Chinese characters being a linguistic unit with semantic information. Contrasting to word-based methods, the computational efficiency of this method is significantly improved at slight loss of accuracy. It is more sententious and cuts off the problems resulted from preparing predefined dictionaries and various data preprocessing.

This article describes the major aspects involved in the development, by Utlas, of a shared cataloguing system capable of handling Chinese ideographic data. Utlas has been involved in the processing of various Asian languages including Japanese and Korean for several years. An outline of the various options available for processing Chinese characters is given. Retrieval mechanisms suitable for the Chinese language are also discussed.

Aims to measure syllable aggregation consistency of Romanized Chinese data in the title fields of bibliographic records. Also aims to verify if the term frequency distributions satisfy conventional bibliometric laws.

Uses Cooper's interindexer formula to evaluate aggregation consistency within and between two sets of Chinese bibliographic data. Compares the term frequency distributions of polysyllabic words and monosyllabic characters (for vernacular and Romanized data) with the Lotka and the generalised Zipf theoretical distributions. The fits are tested with the Kolmogorov‐Smirnov test.

Finds high internal aggregation consistency within each data set but some aggregation discrepancy between sets. Shows that word (polysyllabic) distributions satisfy Lotka's law but that character (monosyllabic) distributions do not abide by the law.

The findings are limited to only two sets of bibliographic data (for aggregation consistency analysis) and to one set of data for the frequency distribution analysis. Only two bibliometric distributions are tested. Internal consistency within each database remains fairly high. Therefore the main argument against syllable aggregation does not appear to hold true. The analysis revealed that Chinese words and characters behave differently in terms of frequency distribution but that there is no noticeable difference between vernacular and Romanized data. The distribution of Romanized characters exhibits the worst case in terms of fit to either Lotka's or Zipf's laws, which indicates that Romanized data in aggregated form appear to be a preferable option.

Provides empirical data on consistency and distribution of Romanized Chinese titles in bibliographic records.

Restaurants’ outdoor signage plays an irreplaceable role in attracting potential diners, as it conveys important functional and symbolic meanings of the businesses. The purpose of this study is to investigate the effect of typographic design elements of outdoor signage on consumers’ perceptions of authenticity. This study also tests the linkage between authenticity and willingness to dine, as well as the moderating effect of frequency of dining in ethnic restaurants on the relationship.

Using a 2 (simplified vs traditional Chinese characters) × 2 (calligraphy vs computer font) × 2 (vertical vs horizontal text flow) between-subject design, the authors did two experiments with 786 Chinese diners. Restaurant authenticity and willingness to dine are dependent variables, and openness to ethnic cuisine is the control variable.

Display characters and text flow significantly affect restaurant authenticity. Furthermore, the results of this study demonstrate that display characters interact with typeface to influence restaurant authenticity. Consumers’ perceived authenticity significantly increases their willingness to dine. The frequency of dining in ethnic restaurants moderates the relationship between restaurant authenticity and willingness to dine.

Ethnic restaurateurs should pay attention to the outdoor signage design, as it affects potential consumers’ authenticity perceptions. Specifically, in Mainland China, traditional Chinese characters and vertical text direction increase potential consumers’ authenticity perceptions.

This study extends the semiotic theory and applies the cue–judgment–behavior model in the hospitality literature. This study also provides new understanding of authenticity by identifying the influence of typographic design on authenticity, which confirms the semiotic theory that certain semiotic cues affect consumers’ judgments.

In the era of Big Data, network digital resources are growing rapidly, especially the short-text resources, such as tweets, comments, messages and so on, are showing a vigorous vitality. This study aims to compare the categories discriminative capacity (CDC) of Chinese language fragments with different granularities and to explore and verify feasibility, rationality and effectiveness of the low-granularity feature, such as Chinese characters in Chinese short-text classification (CSTC).

This study takes discipline classification of journal articles from CSSCI as a simulation environment. On the basis of sorting out the distribution rules of classification features with various granularities, including keywords, terms and characters, the classification effects accessed by the SVM algorithm are comprehensively compared and evaluated from three angles of using the same experiment samples, testing before and after feature optimization, and introducing external data.

The granularity of a classification feature has an important impact on CSTC. In general, the larger the granularity is, the better the classification result is, and vice versa. However, a low-granularity feature is also feasible, and its CDC could be improved by reasonable weight setting, even exceeding a high-granularity feature if synthetically considering classification precision, computational complexity and text coverage.

This is the first study to propose that Chinese characters are more suitable as descriptive features in CSTC than terms and keywords and to demonstrate that CDC of Chinese character features could be strengthened by mixing frequency and position as weight.

The purpose of this study is to examine the influence of semantic fluency on consumers' aesthetic evaluation in graphic designs with text and the mediating effect of visual complexity in this relationship.

The hypotheses are examined in three experiments. Experiments 1 and 2 both verify that Chinese consumers rated the designs with low (vs high) semantic fluency words as more beautiful, and Experiment 3 further confirmed this effect in non-Chinese speakers.

Confirmed by Chinese and non-Chinese consumers, high fluency text leads to lower perceived visual complexity and less aesthetic perception of the entire design.

Findings enrich the theory of beauty standards and put forward challenges to the positive relationship between processing fluency and aesthetic pleasure. Findings are limited to the decorative function of text, and lack discussions on how designers should balance when the informational function of text is equally important.

This study is the first to discuss how designs with text influence consumers' aesthetic perception and provides meaningful guidelines of transnational marketing for fashion designers and enterprises.

This article reviews and discusses issues in the translation of international brand names to Chinese, and provides a framework for international brand managers who want to expand into China. Linguistic differences between Chinese and English are wide and deep, making translation of brand names difficult. Cultural context, pronunciation, written vs. oral language, and the meaning of characters are just a few examples of such difficulties. We discuss four global product‐naming strategic alternatives available to country/brand managers, along with their usage. The four approaches include (1) dual extension, (2) brand meaning extension, (3) brand feeling extension, and (4) dual adaptation. We also provide examples of brands utilizing the different approaches.

This article gives a brief introduction to computer applications in information retrieval, library management and Chinese character processing in some information organisations in China. It outlines the history, present status and future trends of these aspects.