Linguistic Style Research Articles

STYLISTIC STUDY: AN ANALYSIS OF THE SELECTED SONGS OF THE S.I.G.I.T

A study of song interpretation becomes an analysis that prominent about how the author's message is conveyed in the lyrics. This study focuses on analyzing and explaining the style of song lyrics as used and it appears in the selected song lyrics of The S. I. G. I. T. Furthermore, this study also showed the importance of each language style in the lyrics. The research method is a descriptive qualitative method using stylistic theory. This study used listening, reading, and note-taking techniques. Writers are therefore an important means of handling secondary data sources carefully, focused, and exhaustively. The results are dominated by casual and intimate linguistic styles. This study found four types of language styles found within the song lyrics namely allegory, metaphor, epic simile, and also sarcasm.

從檢驗書看穴位詞的語義演變及相關問題

LANGUAGE NOTE | Document text in Chinese; abstract also in English.
 漢語史學界對穴位詞的所知甚少，限於談醫書中穴位名由來。每種文獻的語言風貌有别，反之，從語言可窺知文獻形成的脈絡與書寫態度。有鑑於此，本文在材料與對象上做新嘗試，將檢驗書與穴位做有意義的結合。首先，擴展穴位本體研究，從《洗冤集録》揀出多義穴位詞“肩井”、“肩髃”、“缺盆”、“頰車”、“髮際”、“曲鬢”、“太陽”、“太陽穴”、“鼻山根”、“印堂”、“腰眼”，觀其歷時發展，澄清注解之誤，辨明義位及語法搭配，推測語義演變方向與機制。接著轉换視角，站在檢驗書立場，歸納穴位分布傾向，反思檢驗記録的疑問，及後代的處理態度。本文發現檢驗書的穴位詞出現新義位。單義穴位詞用穴位義，多義穴位詞語義趨向一致，指體表或骨骼。根據書證及語義演變的系統性，筆者認爲穴位義出現在前，體表或骨骼義在後，促使語義變化的機制是轉喻。在滿足交際需求與詞彙空缺兩條件下，促使穴位轉喻出體表或骨骼義。檢驗書穴位的分布集中於臉部，其次是肩部，因爲臉部是驗屍的重點項目。檢驗書的急救法曾以體表定位，是重視實用的表現。基於《洗冤集録》權威地位及檢驗者素質，對此後代選擇原文照録，不另補正，透露了檢驗體系始終堅持實用的價值觀。
 In Chinese language history, studies of the terms used for acupuncture points are scant, and they mostly focused on the origin of the names of acupuncture points found in medical books. As linguistic styles in literature vary, they yield hints to reconstruct the contexts and formation of a text, as well as the writing attitudes of its author. The present essay is a new attempt to deal with both the materials and subjects in an integrated study of the examination books and the names of acupuncture points.
 First, this study expands the ontological research of acupuncture points. From the Xiyuan jilu (Collected Cases of Injustice Rectified), it selects acupuncture point terms with multiple meanings for investigation, including Jianjing (Shaoyang gall bladder meridian of the foot), Jianyu (large intestine meridian of the hand), Quepen (Yangming stomach meridian of the foot), Jiache (Yangming stomach meridian of the foot), Faji (hair line), Qubin (Shaoyang gall bladder meridian of the foot), Taiyang, Taiyang point (temple), Bishangeng (bridge of nose), Yintang (ophryon), and Yaoyan (lumbar). We shall observe their evolution, clarify the errors found in notes, recognize semantic units and syntax combinations, and infer the semantic evolution direction and mechanism. Next, from the perspectives of the examination books, this study generalizes the distribution of acupuncture points and reflects upon the doubts of examination records and later generations’ attitudes towards relevant treatments.
 In this study we realize that some acupuncture point words in the examination books have new semantic units. The acupuncture point terms with a single meaning are so named according to the literal meaning of the acupuncture point. The semantic direction of acupuncture point terms with multiple meanings consistently refers to the bodys surface or bones. According to documentary evidence and the system of semantic evolution, the author argues that the meanings of acupuncture points appear before those of the bodys surface or bones; hence, the semantic change is a metonymy. Under the conditions of interactive demand and lexical gap, acupuncture points are a metonymy of the meanings of the body’s surface or bones.
 In the examination books, the distribution of acupuncture points is mainly to the face, followed by the shoulder; this is because the face is the key to postmortem examination. The first aid method recorded in the examination books was once positioned on the body’s surface; it reveals practicality. Given the authority of Collected Cases of Injustice Rectified and the examiners’ qualification, practitioners of later generations chose to copy the original texts without supplements and corrections. This practice reveals the practical values of the examination system.

The Implication of Gender on Artism: a Review

Purpose: The Agĩkũyũ people of Kenya are one of the largest people groups in Kenya. However, systematic repression of Gĩkũyũ culture led to many traditional elements being forgotten, put aside, if not lost completely. The overall objective of this study was to assess the thematic concerns addressed by Gikuyu secular popular artists on feminist and gender concern.
 Methodology: The paper used a desk study review methodology where relevant empirical literature was reviewed to identify main themes and to extract knowledge gaps.
 Findings: This study concluded that contemporary popular Gikuyu secular artists appropriate the language used in Gikuyu folklore to build their ideas on issues affecting women and men in society. So the linguistic styles of various epochs of literary traditions have proved to be valuable. The contextual analysis of various song-texts earmarked in this study indicates that literature is not only important as a form of entertainment but it is used for purging unpleasant feelings that come from starred relationship between men and women in love relationship, thus prompting of use of images that affect both genders..
 Unique Contribution to Theory and Practice: This study limited itself to three artists and a sample of eighteen songs in exploration of images of women emanating from gender relations espoused in members of the Gikuyu community of Kenya. There are other premiere and also upcoming artists who sing in Gikuyu language. The researcher recommends research on other artists and their song-texts to help shed more light on other aspects of images of women centering on feminism and gender issues in the contemporary society.
 Keywords: Thematic Concerns, Gikuyu, Secular Popular, Artists, Feminist and Gender Concern.

Collective self-understanding: A linguistic style analysis of naturally occurring text data

Understanding what groups stand for is integral to a diverse array of social processes, ranging from understanding political conflicts to organisational behaviour to promoting public health behaviours. Traditionally, researchers rely on self-report methods such as interviews and surveys to assess groups’ collective self-understandings. Here, we demonstrate the value of using naturally occurring online textual data to map the similarities and differences between real-world groups’ collective self-understandings. We use machine learning algorithms to assess similarities between 15 diverse online groups’ linguistic style, and then use multidimensional scaling to map the groups in two-dimensonal space (N=1,779,098 Reddit comments). We then use agglomerative and k-means clustering techniques to assess how the 15 groups cluster, finding there are four behaviourally distinct group types – vocational, collective action (comprising political and ethnic/religious identities), relational and stigmatised groups, with stigmatised groups having a less distinctive behavioural profile than the other group types. Study 2 is a secondary data analysis where we find strong relationships between the coordinates of each group in multidimensional space and the groups’ values. In Study 3, we demonstrate how this approach can be used to track the development of groups’ collective self-understandings over time. Using transgender Reddit data (N= 1,095,620 comments) as a proof-of-concept, we track the gradual politicisation of the transgender group over the past decade. The automaticity of this methodology renders it advantageous for monitoring multiple online groups simultaneously. This approach has implications for both governmental agencies and social researchers more generally. Future research avenues and applications are discussed.

The Use of Bikago Beautified Style in Usage Instructions of Food, Beverage, and Pharmaceutical Products

The current article discusses a linguistic phenomenon, particularly, the use of bikago beautified style, which has been classified as being separate from teineigo since 2007. The discussion covers the forms of this particular linguistic style and the background of its emergence. This research was conducted because, to date, Japanese language studies pertaining to this specific style mostly consisted of researches that are based on spoken data analysis. This is a descriptive-qualitative study. Data were collected from instruction labels of food, beverage, and pharmaceutical products in Japanese provided on the packages of relevant products. Data were gathered based on the content of bikago style element found within a speech. Speeches that do not contain any bikago style element were not included as research data. Research results shows that the number of bikago terms being used on relevant products is very scarce, only consisting of a few words, yet they are distributed in various elements of usage instruction discourse.

Sentiment analysis in tweets: an assessment study from classical to modern word representation models.

With the exponential growth of social media networks, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter - the tweets - have earned significant attention as a rich source of information to guide many decision-making processes. However, their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks, including sentiment analysis. Sentiment classification is tackled mainly by machine learning-based classifiers. The literature has adopted different types of word representation models to transform tweets to vector-based inputs to feed sentiment classifiers. The representations come from simple count-based methods, such as bag-of-words, to more sophisticated ones, such as BERTweet, built upon the trendy BERT architecture. Nevertheless, most studies mainly focus on evaluating those models using only a small number of datasets. Despite the progress made in recent years in language modeling, there is still a gap regarding a robust evaluation of induced embeddings applied to sentiment analysis on tweets. Furthermore, while fine-tuning the model from downstream tasks is prominent nowadays, less attention has been given to adjustments based on the specific linguistic style of the data. In this context, this study fulfills an assessment of existing neural language models in distinguishing the sentiment expressed in tweets, by using a rich collection of 22 datasets from distinct domains and five classification algorithms. The evaluation includes static and contextualized representations. Contexts are assembled from Transformer-based autoencoder models that are also adapted based on the masked language model task, using a plethora of strategies.

Variation in the imperfective in Bahamian English

AbstractThe current study investigates variation in the marking of two aspectual subcategories of the imperfective in Bahamian English. First, it looks into variable auxiliarybeuse in progressive and future constructions, that is, the variation between full, contracted and zerobein non‐past V‐ingenvironments and related contexts. Second, the paper examines variable application of preverbaldoes/is/’sin non‐past habitual environments. The two variables were selected to represent the ‘informal’ and ‘anti‐formal’ group respectively, that is, one feature that classifies as a reduction of English structure and one direct transfer from the creole (Allsopp, 1996, pp. lvi–lvii). Thus, in addition to examining the linguistic constraints, the study will take a close look at the stylistic factors conditioning the variation, placing a special focus on the distribution of the non‐standardized variants over various registers as well as how speakers employ them to create linguistic styles.

Building User-oriented Personalized Machine Translator based on User-Generated Textual Content

Machine Translation (MT) has been a very useful tool to assist multilingual communication and collaboration. In recent years, by taking advantage of the exciting developments of neural networks and deep learning, the accuracy and speed of machine translation have been continuously improved. However, most machine translation methods and systems are data-driven. They tend to select a consensus response represented in training data, while a user's preferred linguistic style, which is important for translation comprehension and user experience, is ignored. For this problem, we aim to build a user-oriented personalized machine translation model in this paper. The model aims to learn each user's linguistic style from the textual content that is generated by her/him (User-Generated Textual Content, UGTC) in social media context and generate personalized translation results utilizing several state-of-the-art deep learning techniques like Transformer and pre-training. We also implemented a user-oriented personalized machine translator using Weibo as a case of the source of UGTC to provide a systematical implementation scheme of a user-oriented personalized machine translation system based on our model. The translator was evaluated by automatic evaluation in combination with human evaluation. The results suggest that our model can generate more personalized, natural and lively translation results and enhance the comprehensibility of translation results, which makes its generations more preferred by users versus general translation results.

The Literal Translation of Sclerometer User Manual from Chinese to Indonesia

The linguistic style and the readers are two vital aspects to consider in applying the literal translation of the ‘Sclerometer User Manual’. These two aspects determine the word choices and their equivalents for the translation of the text to be well perceived by the readers. The descriptive qualitative method was employed for this study with two data sources, the primary data source which was the machine manuals in Chinese and the secondary data source from the documentation. In the process of language transfer, knowledge about Sclerometer, industrial and engineering vocabularies, and understanding of the text to be translated were highly required.

The Thematic Concerns Addressed by Gikuyu Secular Popular Artists on Feminist and Gender Concern: A Critical Literature Review

Purpose: The Agĩkũyũ people of Kenya are one of the largest people groups in Kenya. However, systematic repression of Gĩkũyũ culture led to many traditional elements being forgotten, put aside, if not lost completely. The overall objective of this study was to assess the thematic concerns addressed by Gikuyu secular popular artists on feminist and gender concern.
 Methodology: The paper used a desk study review methodology where relevant empirical literature was reviewed to identify main themes and to extract knowledge gaps.
 Findings: This study concluded that contemporary popular Gikuyu secular artists appropriate the language used in Gikuyu folklore to build their ideas on issues affecting women and men in society. So the linguistic styles of various epochs of literary traditions have proved to be valuable. The contextual analysis of various song-texts earmarked in this study indicates that literature is not only important as a form of entertainment but it is used for purging unpleasant feelings that come from starred relationship between men and women in love relationship, thus prompting of use of images that affect both genders..
 Unique Contribution to Theory and Practice: This study limited itself to three artists and a sample of eighteen songs in exploration of images of women emanating from gender relations espoused in members of the Gikuyu community of Kenya. There are other premiere and also upcoming artists who sing in Gikuyu language. The researcher recommends research on other artists and their song-texts to help shed more light on other aspects of images of women centering on feminism and gender issues in the contemporary society.

Language Use in Mother-Adolescent Dyadic Interaction: Preliminary Results.

This preliminary study applied a computer-assisted quantitative linguistic analysis to examine the effectiveness of language-based classification models to discriminate between mothers (n = 140) with and without history of treatment for depression (51% and 49%, respectively). Mothers were recorded during a problem-solving interaction with their adolescent child. Transcripts were manually annotated and analyzed using a dictionary-based, natural-language program approach (Linguistic Inquiry and Word Count). To assess the importance of linguistic features to correctly classify history of depression, we used Support Vector Machines (SVM) with interpretable features. Using linguistic features identified in the empirical literature, an initial SVM achieved nearly 63% accuracy. A second SVM using only the top 5 highest ranked SHAP features improved accuracy to 67.15%. The findings extend the existing literature base on understanding language behavior of depressed mood states, with a focus on the linguistic style of mothers with and without a history of treatment for depression and its potential impact on child development and trans-generational transmission of depression.

Designing marketing content for social commerce to drive consumer purchase behaviors: A perspective from speech act theory

With the emergence of content-driven social commerce, designing marketing content that better stimulates consumer purchase behaviors has become increasingly essential. However, it remains unclear what and how linguistic features of marketing content in emerging social commerce influence consumer purchase behaviors. Drawing on speech act theory, this paper proposes a multi-level research model to conceptualize the linguistic features of content from the aspects of word usage (micro level), within-content argument development (macro level), and between-content linguistic mimicry (meta level), and investigate their impact on consumer purchase behaviors. With a unique dataset that includes 44,256 textual posts from JD WeChat shopping circle (a content-driven social commerce platform), this paper combines text mining methods with a series of regression analyses to test the research model. The empirical analyses find that the number of customers who make a purchase increases 1) at the micro-level due to self-referencing and detailing, 2) at the macro-level due to argument structuring, and 3) at the meta-level due to linguistic style matching, while linguistic content matching negatively affects the number of customers. These findings reveal how content creators strategically use language to design marketing content that encourages consumer purchase behaviors in emerging social commerce. This study has important theoretical contributions and practical implications.

Joint effect of linguistic style and ethnicity on entrepreneurial fundraising: Evidence from equity crowdfunding

Drawing on both signalling theory and the linguistic framing perspective, we propose that the effectiveness of linguistic styles used by entrepreneurs when pitching their ideas to investors, manifested as funding success, depends on entrepreneurs’ ethnicity (white vs. non-white). Using a sample of 430 equity crowdfunding projects, we found that the use of a past-oriented linguistic style and concrete linguistic style led to greater funding outcomes for ventures with minority founders than it did for those with white founders. A future-oriented linguistic style, on the other hand, was found to be more beneficial for ventures with white founders. In addition, we found a consistent pattern of results using the racial composition of an entrepreneurial team rather than the race of individual founders, which implies that investors’ biases are not only limited to individual founders’ ethnicities, but also persist regarding the wider entrepreneurial team.

Predicting Conversion Rates in Online Hotel Bookings with Customer Reviews

E-commerce in the hospitality and tourism field has already ranked No. 2 among all online shopping categories worldwide. However, customers’ visits to a hotel booking website cannot guarantee the generation of sales, while the conversion rate is regarded as the indicator that effectively assesses the e-commerce website performance. This study aimed to investigate the influential factors of conversion rates from both affective content and the communication style of customer’s online reviews. The affective content was evaluated with eight emotional dimensions (i.e., joy, sadness, anger, fear, trust, disgust, anticipation, and surprise) in Plutchik’s emotion wheel, and the communication style perspective was assessed with linguistic style matching (LSM). In total, 111,926 customer reviews from 641 hotels in five cities in the U.S. were collected for the analysis. Results indicated that LSM and four emotions have significant impacts on hotel conversion rates. This research contributes to the knowledge body of customers’ conversion behaviors on hotel booking websites and offers pertinent practical implications.

Târih-i Gılmânî'nin 17. Yüzyıl Osmanlı Tarih yazımındaki Yeri Üzerine Bir Çalışma

Considering seventeenth century Ottoman historical writing and the place of Mehmed Halife’s Târih-i Gılmânî within it requires one to closely examine the social, economic, and political changes that took place in the seventeenth century Ottoman Empire. It was a century during which the empire was going through a substantial transformation, not only in political and economical terms, but also intellectually. The economic and demographic ramifications of the “seventeenth century crisis” would affect the entire system. Thus, the seventeenth century is considered to have been a crucial era in the history of the Ottoman Empire due to its changing internal dynamics and characteristics that were to give way to the modernization of the Ottoman State. This paper analyzes the place of the Târih-i Gılmânî in seventeenth century history writing emphasizing its linguistic and thematic peculiarities. Like many seventeenth-century chronicles, the Târih-i Gılmânî used the similar stylistic patterns portraying the world as wie es eigentlich gewesen. Nonetheless, one ought to tread carefully when considering the linguistic style of the chronicle, which may include additional revelations between the lines. For example, words carefully chosen may reveal the closeness of the writer to high post officials, who were likely his protectors or commissioners. Therefore, one should be cautious in describing seventeenth century history writing as portraying the world wie es eigentlich gewesen. Nor would it be true to disparage the existence of the sultan’s personality in seventeenth century history writing as a non-issue.

Fake news detection on Twitter

Purpose Owing to the increased accessibility of internet and related technologies, more and more individuals across the globe now turn to social media for their daily dose of news rather than traditional news outlets. With the global nature of social media and hardly any checks in place on posting of content, exponential increase in spread of fake news is easy. Businesses propagate fake news to improve their economic standing and influencing consumers and demand, and individuals spread fake news for personal gains like popularity and life goals. The content of fake news is diverse in terms of topics, styles and media platforms, and fake news attempts to distort truth with diverse linguistic styles while simultaneously mocking true news. All these factors together make fake news detection an arduous task. This work tried to check the spread of disinformation on Twitter. Design/methodology/approach This study carries out fake news detection using user characteristics and tweet textual content as features. For categorizing user characteristics, this study uses the XGBoost algorithm. To classify the tweet text, this study uses various natural language processing techniques to pre-process the tweets and then apply a hybrid convolutional neural network–recurrent neural network (CNN-RNN) and state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) transformer. Findings This study uses a combination of machine learning and deep learning approaches for fake news detection, namely, XGBoost, hybrid CNN-RNN and BERT. The models have also been evaluated and compared with various baseline models to show that this approach effectively tackles this problem. Originality/value This study proposes a novel framework that exploits news content and social contexts to learn useful representations for predicting fake news. This model is based on a transformer architecture, which facilitates representation learning from fake news data and helps detect fake news easily. This study also carries out an investigative study on the relative importance of content and social context features for the task of detecting false news and whether absence of one of these categories of features hampers the effectiveness of the resultant system. This investigation can go a long way in aiding further research on the subject and for fake news detection in the presence of extremely noisy or unusable data.

Linguistic measures of personality in group discussions.

This investigation sought to find the relationships among multiple dimensions of personality and multiple features of language style. Unlike previous investigations, after controlling for such other moderators as culture and socio-demographics, the current investigation explored those dimensions of naturalistic spoken language that most closely align with communication. In groups of five to eight players, participants (N = 340) from eight international locales completed hour-long competitive games consisting of a series of ostensible missions. Composite measures of quantity, lexical diversity, sentiment, immediacy and negations were measured with an automated tool called SPLICE and with Linguistic Inquiry and Word Count. We also investigated style dynamics over the course of an interaction. We found predictors of extraversion, agreeableness, and neuroticism, but overall fewer significant associations than prior studies, suggesting greater heterogeneity in language style in contexts entailing interactivity, conversation rather than solitary message production, oral rather than written discourse, and groups rather than dyads. Extraverts were found to maintain greater linguistic style consistency over the course of an interaction. The discussion addresses the potential for Type I error when studying the relationship between language and personality.

Does it pay off to communicate like your online community? Evaluating the effect of content and linguistic style similarity on B2B brand engagement

Business-to-business (B2B) social media efforts have largely focused on creating brand engagement through online content. We propose to analyse company social media texts (tweets) according to its two main dimensions, content and linguistic style, and to evaluate these in comparison to the overall content and style of the company's community of Twitter followers. We combine 15 million tweets originating from 254,884 followers of ten company profiles and link these to 10,589 B2B company tweets. Using advanced text analytics, we show that content similarity has positive effects on all engagement metrics, while linguistic style similarity mainly affects likes. Readability acts as a moderator for these effects. We also find a negative interaction effect between the similarity metrics, such that style similarity is most useful if content similarity is low. This research is the first to integrate content and linguistic style similarity and contributes to the brand engagement literature by providing practical message composition guidelines, informed by the social media community.

Arranging the fruit basket: A computational approach towards a better understanding of adolescents' diet-related social media communications

This study investigated the contents and linguistic styles that adolescents use in their dietary communications on social media. The main aim of the study was to describe adolescents' social media communication practices with regards to diet, in order to better understand dietary social influences and how to effectively intervene in them. The current study made use of an online communication dataset (N = 72,384 messages) of adolescents in primary and secondary school (N = 1038, 9–16 y/o). We analyzed the message content using tailor-made diet- and health-related dictionaries and LDA topic modeling. The linguistic style of the messages was investigated by assessing the sentiment and underlying psychological dimensions. Further, we used multivariate linear regressions to test how each message dimension was related to message liking, which was considered as an indicator of a message's influence potential regarding peers' dietary behaviors. The results confirmed that adolescents show a preference to discuss neutral-to-unhealthy dietary items most often. No association emerged between message content (i.e., message healthiness and topics) and the number of likes that a message received, while messages with more positive sentiment and higher subjectivity received slightly more likes. Also, our findings confirm that visual dietary content is more often liked than textual descriptions alone. The findings indicate that the content and the healthiness of a message does not explain its popularity among adolescents. Rather, the way how diet-related content is formulated (i.e., in a positive and subjective tone) emerged as a more important predictor for message liking.

Catholic intellectuals in modern China and their Bible translation: Li Wenyu and Ma Xiangbo

AbstractLi Wenyu and Ma Xiangbo were two influential Catholic intellectuals in modern China who each undertook a translation of the Bible into Chinese. This article surveys their participation in contemporary ideological debates and investigates their thoughts on the inculturation of Catholicism, including ‘evangelisation by writing’ and ‘scholastic mission’, as well as their beliefs and practices regarding Bible translation. An in-depth comparison of their translations is also provided, illuminating the different approaches taken by Li and Ma in dealing with terminology, sentence structure, linguistic style, and theological implications. Li conformed to Chinese biblical terminology established by the Catholic missionaries who preceded him and adhered to the syntax of Classical Chinese; in contrast, Ma's linguistic style is approximate to Easy Wenli, and he adopted words and phrases from traditional Chinese culture to transmit the Catholic faith. The two translators demonstrated two choices of indigenising Catholicism and the Bible in China: ‘mirror-type’ and ‘bridge-type’.