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"English language Forecasting."
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The prodigal tongue : dispatches from the future of English
Mark Abley, author of 'Spoken Here', presents an exploration of the way that the English language as it is spoken around the world is likely to transform and be transformed during the 21st century.
Book
Measuring Readability in Financial Disclosures
Defining and measuring readability in the context of financial disclosures becomes important with the increasing use of textual analysis and the Securities and Exchange Commission's plain English initiative. We propose defining readability as the effective communication of valuation-relevant information. The Fog Index—the most commonly applied readability measure—is shown to be poorly specified in financial applications. Of Fog's two components, one is misspecified and the other is difficult to measure. We report that 10-K document file size provides a simple readability proxy that outperforms the Fog Index, does not require document parsing, facilitates replication, and is correlated with alternative readability constructs.
Journal Article
Assessing Public Interpretation of Original and Linguist-Suggested SPC Risk Categories in Spanish
Recent work has shown that the words used in the Storm Prediction Center’s convective outlook are not easily understood by members of the public. Furthermore, Spanish translations of the outlook information have also been shown to have interpretation challenges. This study uses survey data collected from the Severe Weather and Society Spanish Survey, a survey of Spanish speakers across the United States, to evaluate how U.S. residents receive, understand, and respond to weather forecasts and warnings. For this experiment, respondents were tasked with ranking the words and colors used in the SPC’s convective outlook. They were randomly assigned either 1) the words originally used by the SPC for Spanish translations or 2) a set of words suggested by linguistic experts familiar with Spanish dialects in the United States. We find Spanish speakers have similar challenges to English speakers when ordering the words the SPC uses. When using the translations proposed by the linguistic experts, we find the majority of Spanish speakers ranked the words in the intended order of associated risk. Spanish speakers also displayed similar ranking distributions for the colors in the outlook as English speakers, where both groups ranked red as the highest level of risk. These findings suggest the original translations used by the SPC convective outlook create barriers for Spanish speakers and that the expert translations more effectively communicate severe weather hazards to Spanish-speaking members of the public.
Journal Article
Integrating Internet-based applications in English language teaching: Teacher practices in a Thai university
This study was conducted to identify various Internet-based applications integrated in an English as a foreign language (EFL) classroom. It explored how seven English language teachers utilised different applications in their English language teaching (ELT) in a university in Thailand. The methods used were classroom observations and follow-up individual interviews. Findings revealed that Kahoot, Socrative, Google Form, QR code, Facebook, YouTube, Quizizz and Quizlet were among the Internet-based applications the teachers used in their classroom teaching. Moreover, the teachers responded positively to changes in the ELT landscape as they integrated these different applications in their classrooms. They seemed to be confident about the advantages these applications can bring into their teaching practices. Furthermore, their common responses as regards their reason for utilising Internet-based applications point to one evident perspective: Internet-based applications make their ELT classroom more convenient, exciting, and fluid. Implications for ELT are discussed in light of the findings, and recommendations for future research are offered.
Journal Article
Chinese Financial News Analysis for Sentiment and Stock Prediction: A Comparative Framework with Language Models
Financial news has a significant impact on investor sentiment and short-term stock price trends. While many studies have applied natural language processing (NLP) techniques to financial forecasting, most have focused on single tasks or English corpora, with limited research in non-English language contexts such as Taiwan. This study develops a joint framework to perform sentiment classification and short-term stock price prediction using Chinese financial news from Taiwan’s top 50 listed companies. Five types of word embeddings—one-hot, TF-IDF, CBOW, skip-gram, and BERT—are systematically compared across 17 traditional, deep, and Transformer models, as well as a large language model (LLaMA3) fully fine-tuned on the Chinese financial texts. To ensure annotation quality, sentiment labels were manually assigned by annotators with finance backgrounds and validated through a double-checking process. Experimental results show that a CNN using skip-gram embeddings achieves the strongest performance among deep learning models, while LLaMA3 yields the highest overall F1-score for sentiment classification. For regression, LSTM consistently provides the most reliable predictive power across different volatility groups, with Bayesian Linear Regression remaining competitive for low-volatility firms. LLaMA3 is the only Transformer-based model to achieve a positive R2 under high-volatility conditions. Furthermore, forecasting accuracy is higher for the five-day horizon than for the fifteen-day horizon, underscoring the increasing difficulty of medium-term forecasting. These findings confirm that financial news provides valuable predictive signals for emerging markets and that short-term sentiment-informed forecasts enhance real-time investment decisions.
Journal Article
Addressing linguistic diversity in Iranian medical university English for general purposes courses: analyzing the correlation between norm-referenced and criterion-referenced tests
The efficacy of academic English instruction in Iranian medical universities is compromised by the inadequate placement of students into English for General Purposes (EGP) courses, resulting in heterogeneous classes with varying proficiency levels. This study investigated the relationship between norm-referenced tests (NRT) of English proficiency and criterion-referenced tests (CRT) of English achievement, assessed the predictability of CRT scores based on NRT scores, examined the forecasting of one CRT (Final Exam) based on another (Midterm Exam), and compared the performance of students grouped by language proficiency versus academic major. This correlational quantitative study involved 220 paramedical students enrolled in EGP courses. The results indicated significant positive correlations (P < 0.05) between NRT and CRT scores, suggesting that NRT scores can serve as a reliable measure for class placement. Further predictive analyses confirmed the feasibility of forecasting CRT scores using NRT data, with midterm test scores identified as strong predictors of final test performance. Additionally, grouping students based on their proficiency level (Scenario B) exhibited greater language acquisition efficacy than grouping them based on their majors (Scenario A). These findings underscore the importance of implementing NRT-informed placement policies in EGP courses to enhance instructional effectiveness and cater to students’ individual language needs. Adopting proficiency-based grouping can enhance learning outcomes, reduce instructional challenges, and ensure a more equitable language learning environment. This study offers practical implications for curriculum designers, educational policymakers, and instructors seeking to refine English for Specific Purposes (ESP) and English as a Foreign Language (EFL) assessment frameworks, particularly in Asian higher education contexts.
Journal Article
A Memory-Based Neural Network Model for English to Telugu Language Translation on Different Types of Sentences
In India, regional languages play an important role in government-to-public, public-to-citizen rights, weather forecasting and farming. Depending on the state the language also changes accordingly. But in the case of remote areas, the understanding level becomes complex since everything nowadays is presented in the English Language. In such conditions, the regional language manual translation consumes more time to provide services to the common people. The automatic translation of one language to another by maintaining the meaning of the given input sentence there by producing the exact meaning in the output language is carried out through Machine Translation. In this work, we proposed a Memory Based Neural Network for Translation (MBNNT) model on simple, compound and complex sentences for English to Telugu language translation. We used BLEU and WER metrics for identifying the translation quality. On applying these metrics over different type of sentences LSTM showed promising results over Statistical Machine Translation and Recurrent Neural Networks in terms of the quality and performance.
Journal Article
Emotional sentiment analysis of social media content for mental health safety
Text sentiment analysis is mostly used for the assessment of the author’s mood depending on the context. The purpose of sentiment analysis is to discover the exactness of the underlying emotion in a given situation. It has been applied to various fields, including stock market predictions, social media data on product evaluations, psychology, the judiciary, forecasting, illness prediction, agriculture, and more. Many researchers have worked on these topics and generated important insights. These outcomes are useful in the field because they (outcomes) help people comprehend the general summary quickly. Additionally, sentiment analysis aids in limiting the harmful effects of some posts on various social media sites such as Facebook and Twitter. For these reasons and more, we are proposing an approach to filter the social media content that could be emotionally harmful to the user, through getting the social networks content; for that, we have used Twitter API to get the user posts, and then, we have used API natural understanding language API tool to extract and classify the emotions of the Twitter content into five basic emotional categories—Joy, sadness, anger, fear, disgust—into an array of emotions; after that, we have defined a perfect emotion array from over 450 words from the English language. The main purpose of this comprehensive research article is to examine the proposed solution that we have conducted to improve the quality of content displayed to users emotionally.
Journal Article
Seasonality and Interannual Variability of the Westerly Jet in the Tibetan Plateau Region
In this study, 40-yr ECMWF Re-Analysis (ERA-40) data are used for the description of the seasonal cycle and the interannual variability of the westerly jet in the Tibetan Plateau region. To complement results based on the analysis of monthly mean horizontal wind speeds, an occurrence-based jet climatology is constructed by identifying the locations of the jet axes at 6-hourly intervals throughout 1958–2001. Thus, a dataset describing the highly transient and localized features of jet variability is obtained.
During winter and summer the westerly jet is located, respectively, to the south and north of the Tibetan Plateau. During the spring and autumn seasons there are jet transitions from south to north and vice versa. The median dates for these transitions are 28 April and 12 October. The spring transition is associated with large interannual variations, while the fall transition occurs more reliably within a 3-week period. The strength of the jet exhibits a peculiar seasonal cycle. During northward migration in April/May, the jet intensity weakens and its latitudinal position varies largely. In some springs, there are several transitions and split configurations occur before the jet settles in its northern summer position. In June, a well-defined and unusually strong jet reappears at the northern flanks of the Tibetan Plateau. In autumn, the jet gradually but reliably recedes to the south and is typically more intense than in spring.
The jet transitions between the two preferred locations follow the seasonal latitudinal migration of the jet in the Northern Hemisphere. An analysis of interannual variations shows the statistical relationship between the strength of the summer jet, the tropospheric meridional temperature gradient, and the all-India rainfall series. Both this analysis and results from previous studies point to the particular dynamical relevance of the onsetting Indian summer monsoon precipitation and the associated diabatic heating for the formation of the strong summer jet.
Finally, an example is provided that illustrates the climatological significance of the jet in terms of the covariation between the jet location and the spatial precipitation distribution in central Asia.
Journal Article
Acceptance of ChatGPT by undergraduates in Sri Lanka: a hybrid approach of SEM-ANN
PurposeThis study aims to investigate Sri Lankan Government university students’ acceptance of Chat Generative Pretrained Transformer (ChatGPT) for educational purposes. Using the unified theory of acceptance and use of technology 2 (UTAUT2) model as the primary theoretical lens, this study incorporated personal innovativeness as both a dependent and moderating variable to understand students’ ChatGPT use behaviour.Design/methodology/approachThis quantitative study used a questionnaire survey to collect data. A total of 500 legitimate undergraduates from 17 government universities in Sri Lanka were selected for this study. Items for the variables were adopted from previously validated instruments. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4 was used to investigate latent constructs’ relationships. Furthermore, the variables’ relative relevance was ranked using a two-stage artificial neural network analysis with the SPSS 27 application.FindingsThe results of the analysis revealed that eight of the nine proposed hypotheses were confirmed. The most significant determinants of behavioural intention were habit and performance expectancy, closely followed by hedonic motivation and perceived ease of use. Use behaviour was highly influenced by both behavioural intention and personal inventiveness. Though personal innovativeness (PI) was suggested as a moderator, the relationship was not significant.Research limitations/implicationsThe research highlights the impact of habit, performance expectancy and perceived ease of use on students’ acceptance of AI applications such as ChatGPT, emphasising the need for efficient implementation techniques, individual variations in technology adoption and continuous support and training to improve students’ proficiency.Originality/valueThis study enhances the comprehension of how undergraduate students adopt ChatGPT in an educational setting. The study emphasises the significance of certain variables in the UTAUT2 model and the importance of PI in influencing the adoption of ChatGPT in educational environments.
Journal Article