Enabling the language of accessible tourism

The growing body of literature on “accessible tourism” lacks a critical scholarly debate around its specific language use and nomenclatures. To fill this gap, this paper provides a first examination of language. Language provides a unique capability to resist, strengthen and reframe identities of individuals and groups, yet can also reinforce, weaken and perpetuate dominant worldviews of disability. A content analysis examined previous accessible tourism literature with results illustrating that diversity exists amongst the varying terminologies adopted by scholars. Terms were employed loosely, inconsistently and interchangeably, euphemistically with erroneous understandings and nuances. The paper concludes with critical discussion about the power of researchers to (re) produce oppression through language that maligns and misrepresents, or to (re) conceptualise and (re) construct the world we live in with liberating language that facilitates positive social change.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

Linking words in economic discourse: Implications for macroeconomic forecasts

This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.     

Unraveling the interplay of review depth,review breadth,and review language style on review usefulness and review adoption

To examine whether long reviews are uniformly more useful to readers, this study utilizes the experimental design approach to investigate the interplay of review depth, review breadth, and review language style on readers’ perceived usefulness and adoption intention of online hotel reviews. Drawing on the results from two experiments, review depth is proven to exert a positive impact on readers’ review usefulness and adoption intention. The moderating role of review breadth is verified, and the positive effect of review depth on review usefulness is attenuated (accentuated) when review breadth is high (low). The moderating role of review language style is also confirmed, and the result shows longer reviews written in literal language are considered more useful to readers than those written in figurative language. Being one of the first studies examining the interplay of review content and review style on review usefulness, this study provides important theoretical and practical contributions.     

跨境电商英语沟通中的语用移情策略研究

跨境电商的飞速发展离不开跨境电商英语沟通质量的提升,跨境电商英语沟通是在交流过程中,买卖双方传递、共享信息和感情的过程。运用语用学的移情理论,结合跨境电商英语沟通特点,从礼貌语、委婉语、模糊语和幽默语等方面,建构语用移情策略,有助于缩短买卖双方的心理距离,达到预期的语用效果,推动畅通有效地跨境电商沟通。     

对戴维森的意义理论的评述

戴维森以塔斯基的真理论为基础,提出了自己著名的意义理论———成真条件语义学。戴维森的成真条件语义学是对塔斯基的真理论的继承和发展,对语言哲学的发展起了重大的作用。     

浅谈学习中心向动态中心的转移

“教师中心论”、“学生中心论”以及“学习中心论”教学法有其各自的理论基础及不足,动态学习中心的观点认为,学生的角色与教师的角色是动态的,二者相互促进、相互作用。     

客体语言的中西文化比较

客体语言的使用具有宗教文化作用、昭示规范作用和社会心理作用。从特定文化的物象和事象中对客体语言加以探讨,有利于不同文化间的理解和交流,使不同民族精神和文化传统得以共存和共赏。     

二外日语教学艺术初探

进行二外日语教学需要讲究教学艺术。采用日语歌曲和日语学习软件辅助日语教学,并合理地借助英语进行日语教学,有利于提高教学效果。     

母语水平与外语翻译能力的相关性

采用试卷及问卷进行调查,用统计软件对所搜集的数据进行相关性分析,并进行回归预测,研究分析发现我国大学生母语水平与外语翻译能力之间有显著的相关性(r=0.66),母语水平能够解释43.6%的翻译水平差异。