Search results for: multilingual sentiment analysis

Authors: Lisa Graham, Kathleen Grant

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South Africa is a multicultural country with eleven official languages, yet most of the formal education at institutions of higher education in the country is in English. It is well known that many students, irrespective of their home language, struggle to grasp difficult scientific concepts and the same is true for students enrolled in the Extended Curriculum Programme at the Cape Peninsula University of Technology (CPUT), studying biomedical sciences. Today we are fortunate in that there is a plethora of resources available to students to research and better understand subject matter online. For example, the students often use YouTube videos to supplement the formal education provided in our course. Unfortunately, most of this material is presented in English. The rationale behind this project lies in that it is well documented that students think and grasp concepts easier in their home language and addresses the fact that the lingua franca of instruction in the field of biomedical science is English. A project aimed at addressing the lack of available resources in most of the South African languages is planned, where students studying Bachelor of Health Science in Medical Laboratory Science will collaborate with those studying Film and Video Technology to create educational videos, explaining scientific concepts in their home languages. These videos will then be published on our own YouTube channel, thereby making them accessible to fellow students, future students and anybody with interest in the subject. Research will be conducted to determine the benefit of the project as well as the published videos to the student community. It is suspected that the students engaged in making the videos will benefit in such a way as to gain further understanding of their course content, a broader appreciation of the discipline, an enhanced sense of civic responsibility, as well as greater respect for the different languages and cultures in our classes. Indeed, an increase in student engagement has been shown to play a central role in student success, and it is well noted that deeper learning and more innovative solutions take place in collaborative groups. We aim to make a meaningful contribution towards the production and repository of knowledge in multilingual teaching and learning for the benefit of the diverse student population and staff. This would strengthen language development, multilingualism, and multiculturalism at CPUT and empower and promote African languages as languages of science and education at CPUT, in other institutions of higher learning, and in South Africa as a whole.

Keywords: educational videos, multiculturalism, multilingualism, student engagement

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Authors: Zixi You

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With the demand of multilingual speakers increasing in the job market, multi-language learning programs have become more and more popular among undergraduate students. A study on multi-language learning strategies is therefore highly demanded on both practical and theoretical levels. Based on previous classification of learning strategies in SLA, and an investigation of BA Modern Language program students (with post-A level L2 and ab initio L3 learning experience from year one), this study explores and compares different types of learning strategies used by multi-language speakers and learners, transferable learning strategies between L2 and L3, and factors affecting the transfer. The results indicate that all the 23 types of learning strategies of L2 are employed when learning L3 from ab initio level, yet with different tendencies. Learning strategy transfer from L2 to L3 (i.e., the learners attribute the applying of these L3 learning strategies to be a direct result of their L2 learning experience) are observed in all 23 types of learning strategies. Comparatively, six types of “cognitive strategies” have higher transfer tendency than others. With regard to the failure of the transfer of some particular L2 strategies and the development of independent L3 strategies of individual learners, factors such as language proficiency, language typology and learning environment have played important roles among others. The presentation of this study will provide audiences with detailed data, insightful analysis and discussion on both theoretical and practical aspects of multi-language learning that will benefit both students and educators.

Keywords: learning strategy, multi-language acquisition, second language acquisition, strategy transfer

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Authors: Kamal Amarouche, Houda Benbrahim, Ismail Kassou

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Nowadays, e-commerce shopping websites have experienced noticeable growth. These websites have gained consumers’ trust. After purchasing a product, many consumers share comments where opinions are usually embedded about the given product. Research on the automatic management of opinions that gives suggestions to potential consumers and portrays an image of the product to manufactures has been growing recently. After launching the product in the market, the reviews generated around it do not usually contain helpful information or generic opinions about this product (e.g. telephone: great phone...); in the sense that the product is still in the launching phase in the market. Within time, the product becomes old. Therefore, consumers perceive the advantages/ disadvantages about each specific product feature. Therefore, they will generate comments that contain their sentiments about these features. In this paper, we present an unsupervised method to extract different product features hidden in the opinions which influence its purchase, and that combines Time Weighting (TW) which depends on the time opinions were expressed with Term Frequency-Inverse Document Frequency (TF-IDF). We conduct several experiments using two different datasets about cell phones and hotels. The results show the effectiveness of our automatic feature extraction, as well as its domain independent characteristic.

Keywords: opinion mining, product feature extraction, sentiment analysis, SentiWordNet

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Authors: Binay Pattanayak

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More than 9 out of every 10 children in Jharkhand struggle to understand the texts and teachers in public schools. The medium of learning in the schools is Hindi, which is very different in structure and vocabulary than those in children’s home languages. Hence around 3 out of 10 children enrolled in early grades drop out in these schools. The state realized the cause of children’s high dropout in 2013-14 when the M-TALL, the language research shared the findings of a state-wide socio-linguistic study. The study findings suggested that there was a great need for initiating a mother-tongue based multilingual education (MTB-MLE) programme for the state in early grades starting from pre-school level. Accordingly, M-TALL in partnership with department of education designed two learning packages: Bhasha Puliya pre-school education programme for 3-6-year-old children for their school readiness with bilingual picture dictionaries in 9 tribal and regional languages. This was followed by a plan for MTB-MLE programme for early primary grades. For this textbooks in five tribal and two regional languages were developed under the guidance of the author. These books were printed and circulated in the 1000 schools of the state for each child. Teachers and community members were trained for facilitating culturally sensitive mother-tongue based learning activities in and around the schools. The mother-tongue based approach of learning has worked very effectively in enabling them to acquire the basic literacy and numeracy skills in own mother-tongues. Using this basic early grade reading skills, these children are able to learn Hindi and English systematically. Community resource groups were constituted in each school for promoting storytelling, singing, painting, dancing, acting, riddles, humor, sanitation, health, nutrition, protection, etc. and were trained. School academic calendar was designed in each school to enable the community resource persons to visit the school as per the learning plan to assist children and teacher in facilitating rich cultural activities in mother-tongue. This enables children to take part in plethora of learning activities and acquire desired knowledge, skills and interest in mother-tongues. Also in this process, it is attempted to promote 21st Century learning skills by enabling children to apply their new knowledge and skills to look at their local issues and address those in a collective manner through team work, innovations and leadership.

Keywords: community resource groups, learning, MTB-MLE, multilingual, socio-linguistic survey

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Authors: Mobina Sahraee Juybari

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There are around 300 languages spoken in Australia, and more than one-fifth of the population speaks a language other than English at home. The presence of international students in schools raises this number still further. Although the multilingual and multicultural status of Australia has been acknowledged by the government in education policy, the strong focus on English in institutional settings threatens the maintenance and learning of other languages. This is particularly true of universities’ language provisions. To cope with the financial impact of Covid-19, the government has cut funding for a number of Asian languages, such as Indonesian, Japanese and Chinese. This issue threats the maintenance of other languages in Australia and leaves students unprepared for the future job market. By taking account of the current reality of Australia’s diverse cultural and lingual makeup, this research intends to uncover the positioning of languages by having a historical look at Australia’s language policy and examining the value of languages and the probable impact of Covid-19 on the place of languages taught in Australian universities. A qualitative study will be adopted with language program tutors and course coordinators, with semi-structured interviews and government language policy analysis. This research hopes to provide insights into both the maintenance and learning of international language programs in tertiary language education in Australia and more widely.

Keywords: Australia, COVID-19, higher education sector, language maintenance, language and culture diversity

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Authors: Simon Huston, Ebraheim Lahbash, Ali Parsa

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The development of the United Arab Emirates (UAE) into a regional trade, tourism, finance and logistics hub has transformed its real estate markets. However, speculative activity and price volatility remain concerns. UAE residential market values (MV) are exposed to fluctuations in capital flows and migration which in turn are affected by geopolitical uncertainty, oil price volatility, and global investment market sentiment. Internally, a complex interplay between administrative boundaries, land tenure, building quality and evolving location characteristics fragments UAE residential property markets. In short, the UAE Residential Valuation System (UAE-RVS) confronts multiple challenges to collect, filter and analyze relevant information in complex and dynamic spatial and capital markets. A robust (RVS) can mitigate the risk of unhelpful volatility, speculative excess or investment mistakes. The research outlines the institutional, ontological, dynamic, and epistemological issues at play. We highlight the importance of system capabilities, valuation standard salience and stakeholders trust.

Keywords: valuation, property rights, information, institutions, trust, salience

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Authors: Abdelbaset Rabaiah

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e-Government mobile applications provide an extension for effective e-government services in today’s omniconnected world. They constitute part of m-government platforms. This study explores the usefulness, availability, discoverability and maturity of such applications. While this study impacts theory by addressing a relatively lacking area, it impacts practice more. The outcomes of this study suggest valuable recommendations for practitioners-developers of e-government applications. The methodology followed is to examine a large number of e-government smartphone applications. The focus is on applications available at the Google Play Store. Moreover, the study investigates applications published on government portals of a number of countries. A sample of 15 countries is researched. The results show a diversity in the level of discoverability, development, maturity, and usage of smartphone apps dedicated for use of e-government services. It was found that there are major issues in discovering e-government applications on both the Google Play Store and as-well-as on local government portals. The study found that only a fraction of mobile government applications was published on the Play Store. Only 19% of apps were multilingual, and 43% were developed by third parties including private individuals. Further analysis was made, and important recommendations are suggested in this paper for a better utilization of e-government smartphone applications. These recommendations will result in better discoverability, maturity, and usefulness of e-government applications.

Keywords: mobile applications, e-government, m-government, Google Play Store

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Authors: Migena Ceyhan, Zeynep Orhan, Dimitrios Karras

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People in modern societies are continuously sharing their experiences, emotions, and thoughts in different areas of life. The information reaches almost everyone in real-time and can have an important impact in shaping people’s way of living. This phenomenon is very well recognized and advantageously used by the market representatives, trying to earn the most from this means. Given the abundance of information, people and organizations are looking for efficient tools that filter the countless data into important information, ready to analyze. This paper is a modest contribution in this field, describing the process of automatically classifying social media comments in the Turkish language into positive or negative. Once data is gathered and preprocessed, feature sets of selected single words or groups of words are build according to the characteristics of language used in the texts. These features are used later to train, and test a system according to different machine learning algorithms (Naïve Bayes, Sequential Minimal Optimization, J48, and Bayesian Linear Regression). The resultant high accuracies can be important feedback for decision-makers to improve the business strategies accordingly.

Keywords: feature selection, machine learning, natural language processing, sentiment analysis, social media reviews

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

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During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

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Authors: Zana Llonçari Osmani, Nita Llonçari

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In Kosovo, public rhetoric and popular sentiment position the War of 1998-99 (the war) as central to the formation of contemporary Kosovo's national identity. This period was marked by the forced massive displacement of Kosovo Albanians, the destruction of entire settlements, the loss of family members, and the profound emotional trauma experienced by civilians, particularly those who actively participated in the war as members of the Kosovo Liberation Army (KLA). Amidst these profound experiences, the Prekaz Massacre (The Massacre) is widely regarded as the defining event that preceded the final struggles of 1999 and the long-awaited attainment of independence. This study aims to explore how different visitors perceive the dominant discourse at The Memorial, a site dedicated to commemorating the Prekaz Massacre, and to identify the factors that influence their perceptions. The research employs a comprehensive mixed-method approach, combining online surveys, critical discourse analysis of visitor impressions, and content analysis of media representations. The findings of the study highlight the significant role played by original material remains in shaping visitor perceptions of The Memorial in comparison to the curated symbols and figurative representations interspersed throughout the landscape. While the design elements and physical layout of the memorial undeniably hold significance in conveying the memoryscape, there are notable shortcomings in enhancing the overall visitor experience. Visitors are still primarily influenced by the tangible remnants of the war, suggesting that there is room for improvement in how design elements can more effectively contribute to the memorial's narrative and the collective memory of the Prekaz Massacre.

Keywords: critical discourse analysis, memorialisation, national discourse, public rhetoric, war tourism

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Authors: Heba M. Wagih, Hoda M. O. Mokhtar

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Social Network has recently played a significant role in both scientific and social communities. The growing adoption of social network applications has been a relevant source of information nowadays. Due to its popularity, several research trends are emerged to service the huge volume of users including, Location-Based Social Networks (LBSN), Recommendation Systems, Sentiment Analysis Applications, and many others. LBSNs applications are among the highly demanded applications that do not focus only on analyzing the spatiotemporal positions in a given raw trajectory but also on understanding the semantics behind the dynamics of the moving object. LBSNs are possible means of predicting human mobility based on users social ties as well as their spatial preferences. LBSNs rely on the efficient representation of users’ trajectories. Hence, traditional raw trajectory information is no longer convenient. In our research, we focus on studying human behavior trajectory which is the major pillar in location recommendation systems. In this paper, we propose an ontology design patterns with their underlying description logics to efficiently annotate human behavior trajectories.

Keywords: human behavior trajectory, location-based social network, ontology, social network

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Authors: Gurjeet Singh, Harinder Singh

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In this paper, we present "The Language of Science in Higher Education: Related Questions and Discussions". Linguists have written and researched in depth the role of language in science. On this basis, it is clear that language is not just a medium or vehicle for communicating knowledge and ideas. Nor are there mere signs of language knowledge and conversion of ideas into code. In the process of reading and writing, everyone thinks deeply and struggles to understand concepts and make sense. Linguistics play an important role in achieving concepts. In the context of such linguistic diversity, there is no straightforward and simple answer to the question of which language should be the language of advanced science and technology. Many important topics related to this issue are as follows: Involvement in practical or Deep theoretical issues. Languages for the study of science and other subjects. Language issues of science to be considered separate from the development of science, capitalism, colonial history, the worldview of the common man. The democratization of science and technology education in India is possible only by providing maximum reading/resource material in regional languages. The scientific research should be increase to chances of understanding the subject. Multilingual instead or monolingual. As far as deepening the understanding of the subject is concerned, we can shed light on it based on two or three experiences. An attempt was made to make the famous sociological journal Economic and Political Weekly Hindi almost three decades ago. There were many obstacles in this work. The original articles written in Hindi were not found, and the papers and articles of the English Journal were translated into Hindi, and a journal called Sancha was taken out. Equally important is the democratization of knowledge and the deepening of understanding of the subject. However, the question is that if higher education in science is in Hindi or other languages, then it would be a problem to get job. In fact, since independence, English has been dominant in almost every field except literature. There are historical reasons for this, which cannot be reversed. As mentioned above, due to colonial rule, even before independence, English was established as a language of communication, the language of power/status, the language of higher education, the language of administration, and the language of scholarly discourse. After independence, attempts to make Hindi or Hindustani the national language in India were unsuccessful. Given this history and current reality, higher education should be multilingual or at least bilingual. Translation limits should also be increased for those who choose the material for translation. Writing in regional languages on science, making knowledge of various international languages available in Indian languages, etc., is equally important for all to have opportunities to learn English.

Keywords: language, linguistics, literature, culture, ethnography, punjabi, gurmukhi, higher education

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Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

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Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

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Authors: M. Vassou, A. Karasimos

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A primary concern in the field of Second Language Acquisition (SLA) research is to determine the innate mechanisms of second language learning and acquisition through the systematic study of a learner's interlanguage. Errors emerge while a learner attempts to communicate using the target-language and can be seen either as the observable linguistic product of the latent cognitive and language process of mental representations or as an indispensable learning mechanism. Therefore, the study of the learner’s erroneous forms may depict the various strategies and mechanisms that take place during the language acquisition process resulting in deviations from the target-language norms and difficulties in communication. Mapping the erroneous utterances of a late adult learner in the process of acquiring Greek as a second language constitutes one of the main aims of this study. For our research purposes, we created an error-tagged learner corpus composed of the participant’s written texts produced throughout a period of a 4- year instructed language acquisition. Error analysis and interlanguage theory constitute the methodological and theoretical framework, respectively. The research questions pertain to the learner's most frequent errors per linguistic category and per year as well as his choices concerning the Greek Article System. According to the quantitative analysis of the data, the most frequent errors are observed in the categories of the stress system and syntax, whereas a significant fluctuation and/or gradual reduction throughout the 4 years of instructed acquisition indicate the emergence of developmental stages. The findings with regard to the article usage bespeak fossilization of erroneous structures in certain contexts. In general, our results point towards the existence and further development of an established learner’s (inter-) language system governed not only by mother- tongue and target-language influences but also by the learner’s assumptions and set of rules as the result of a complex cognitive process. It is expected that this study will contribute not only to the knowledge in the field of Greek as a second language and SLA generally, but it will also provide an insight into the cognitive mechanisms and strategies developed by multilingual learners of late adulthood.

Keywords: Greek as a second language, error analysis, interlanguage, late adult learner

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Authors: Ricky Leung

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Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve the health and well-being of individuals and communities. One way AI can be used to enhance social media in health organizations is through sentiment analysis. This involves analyzing the emotions expressed in social media posts to better understand public opinion and respond accordingly. This can help organizations gauge the impact of their campaigns, track the spread of misinformation, and improve communication with the public. While social media is a useful tool, researchers and practitioners have expressed fear that it will be used for the spread of misinformation, which can have serious consequences for public health. Health organizations must work to ensure that AI systems are transparent, trustworthy, and unbiased so they can help minimize the spread of misinformation. In conclusion, AI and ML have the potential to greatly enhance the use of social media in health organizations. These technologies can help organizations effectively manage large amounts of data and understand stakeholders' sentiments. However, it is important to carefully consider the potential consequences and ensure that these systems are carefully designed to minimize the spread of misinformation.

Keywords: AI, ML, social media, health organizations

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Authors: Humaira Irfan Khan

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The paper discusses that Pakistan is a multilingual, multicultural, and multiethnic society. The findings from quantitative and qualitative data collected in two public universities look at the importance of English language and the role and status of national and regional languages in the country. The evidence implies that postgraduate students having diverse linguistic, cultural, ethnic, socio-economic, and educational backgrounds display negative attitudes towards the use of English language for academic and interactive functions in universities. It is also discovered that language anxiety of postgraduate students is an outcome of their language learning difficulties. It is suggested that considering the academic needs of students, universities should introduce a language proficiency course to enable them to use English with confidence.

Keywords: Multilingualism, Ethnicity, Cultural Diversity, Importance of English, National language, Regional languages, Language Anxiety

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Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

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Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand the smallholder farming communities within Scotland by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted in conjunction with text mining of the data in search of common themes, words, and topics found within the text. Results from bi-grams and topic modelling uncover four main topics of interest within the data pertaining to aspects of livestock husbandry: feeding, breeding, slaughter, and disposal. These topics were found amongst both the poultry and pig sub-forums. Topic modeling appears to be a useful method of unsupervised classification regarding this form of data, as it has produced clusters that relate to biosecurity and animal welfare. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter and Facebook/Meta, in addition to time series analysis to highlight temporal patterns.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, smallholding, social media, web scraping, sentiment analysis, geolocation, text mining, NLP

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Authors: Nnaemeka Enemchukwu

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In modern-day Nigeria, the spirit of Biafra, the defunct secessionist state of former Eastern Nigeria, endures. While some attempt to downplay the historical factors that led to its creation, this paper aims to demonstrate that the 1966 pogroms in Nigeria, which claimed the lives of over 30,000 Igbo people, shattered their faith in the nation's ability to provide security and acceptance. This loss of faith led to a mass exodus from various regions of the country back to their homeland in Eastern Nigeria. Utilizing primary sources such as interviews and archival reports, and secondary sources like books, journals, and websites, this paper will argue that the trauma and terror of the 1966 massacres were the primary drivers of secessionist sentiment and self-determination among the Igbo people, ultimately leading to the declaration of Biafra. By drawing parallels with other historical incidents across the globe, this paper will establish the theoretical connection between shocking events, identity questioning among traumatized groups, and the subsequent rise of nationalistic sentiments seeking to ensure group preservation. To achieve its objective, this paper will employ descriptive, narrative, and chronological methods of analysis to present and discuss its findings.

Keywords: Igbo, pogrom, shock, trauma, nationalism, Biafra

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Authors: Ayşe Yurdakul, Eckehard Schnieder

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Nowadays, there are multilingual and multidisciplinary communication problems because of the increasing technical progress. Each technical field has its own specific terminology and in each particular language, there are differences in relation to definitions of terms. Besides, there could be several translations in the certain target language for one term of the source language. First of all, these problems of semantic relations between terms include the synonymy, antonymy, hypernymy/hyponymy, ambiguity, risk of confusion and translation problems. Therefore, the iglos terminology management system of the Institute for Traffic Safety and Automation Engineering of the Technische Universität Braunschweig has the goal to avoid these problems by a methodological standardisation of term definitions on the basis of the iglos sign model and iglos relation types. The focus of this paper should be on standardisation of navigation terminology as an example.

Keywords: iglos, localisation, methodological approaches, navigation, positioning, definition problems, terminology

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Authors: Ward Makhoul

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Code-switching (CS) is known as a ubiquitous phenomenon in multilingual societies and countries. Vernacular Palestinian Arabic (PA) variety spoken in Israel is among these languages, informally used for day-to-day conversations only. Such conversations appear to contain code-switched instances from Hebrew, the formal and dominant language of the country, even in settings where the need for CS seems to be unnecessary. This study examines the CS practices in PA and investigates the reason behind these CS instances in controlled settings and the correlation between bilingual dominance and CS. In the production-task interviews and Bilingual Language Profile test (BLP), there was a correlation between language dominance and CS; 13 participants were interviewed to elicit and analyze natural speech-containing CS instances, along with undergoing a BLP test. The acceptability judgment task observed the limits and boundaries of different code-switched linguistic structures.

Keywords: code-switching, Hebrew, Palestinian-Arabic, vernacular

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Nada Alghali

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Translanguaging pedagogy suggests a new perspective in language education relating to multilingualism; multilingual learners have one linguistic repertoire and not two or more separate language systems (García and Wei, 2014). When learners translanguage, they are able to draw on all their language features in a flexible and integrated way (Otheguy, García, & Reid, 2015). In the Foreign Language Classroom, however, the tendency to use the target language only is still advocated as a pedagogy. This study attempts to enable learners in the English as a foreign language classroom to draw on their full linguistic repertoire through collaborative reading lessons. In observations prior to this study, in a classroom where English only policy prevails, learners still used their first language in group discussions yet were constrained at times by the teacher’s language policies. Through strategically enabling translanguaging in reading lessons (Celic and Seltzer, 2011), this study has revealed that learners showed creative ways of language use for learning and reflected positively on thisexperience. This case study enabled two groups in two different proficiency level classrooms who are learning English as a foreign language in their first year at University in Saudi Arabia. Learners in the two groups wereobserved over six weeks and wereasked to reflect their learning every week. The same learners were also interviewed at the end of translanguaging weeks after completing a modified model of the learning reflection (Ash and Clayton, 2009). This study positions translanguaging as collaborative and agentive within a sociocultural framework of learning, positioning translanguaging as a resource for learning as well as a process of learning. Translanguaging learning episodes are elicited from classroom observations, artefacts, interviews, reflections, and focus groups, where they are analysed qualitatively following the sociocultural discourse analysis (Fairclough &Wodak, 1997; Mercer, 2004). Initial outcomes suggest functions of translanguaging in collaborative reading tasks and recommendations for a collaborative translanguaging pedagogy approach in the EFL classroom.

Keywords: translanguaging, EFL, sociocultural theory, discourse analysis

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Authors: Marilei Amadeu Sabino

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Before the advent of Cognitive Linguistics, metaphor was considered a stylistic issue, but now it is viewed as a critical component of everyday language and a fundamental mechanism of human conceptualizations of the world. It means that human beings' conceptual system (the way we think and act) is metaphorical in nature. Another interesting hypothesis in Cognitive Linguistics is that cognition is embodied, that is, our cognition is influenced by our experiences in the physical world: the mind is connected to the body and the body influences the mind. In this sense, it is believed that many conceptual metaphors appear to be potentially universal or near-universal, because people across the world share certain bodily experiences. In these terms, many metaphors may be identical or very similar in several languages. Thus, in this study, we analyzed some somatic (also called body) idioms of Italian and Portuguese languages, in order to investigate the proportion in which their metaphors are the same, similar or different in both languages. It was selected hundreds of Italian idioms in dictionaries and indicated their corresponding idioms in Portuguese. The analysis allowed to conclude that much of the studied expressions are really structurally, semantically and metaphorically identical or similar in both languages. We also contrasted some Portuguese and Italian somatic expressions to their corresponding English idioms to have a multilingual perspective of the issue, and it also led to the conclusion that the most common idioms based on metaphors are probably those that have to do with the human body. Although this is mere speculation and needs more study, the results found incite relevant discussions on issues that matter Foreign and Second Language Teaching and Learning, including the retention of vocabulary. The teaching of the metaphorically different body idioms also plays an important role in language learning and teaching as it will be shown in this paper. Acknowledgments: FAPESP – São Paulo State Research Support Foundation –the financial support offered (proc. n° 2017/02064-7).

Keywords: body idioms, cognitive linguistics, metaphor, vocabulary teaching and learning

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Authors: Jabulani Sibanda, Clemence Chikiwa

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This paper traces diverse language practices representative of equally diverse conceptions of language. To be dynamic with languaging practices, one needs to appreciate nuanced languaging practices, their challenges, prospects, and opportunities. The paper presents what we envision as three major conceptions of language that give impetus to diverse language practices. It examines theoretical models of the bilingual mental lexicon and how they inform language practices. The paper explores classroom languaging practices that have been promulgated and experimented with. The paper advocates the deployment of multisensory semiotic systems to complement linguistic classroom communication and the acknowledgement of learners’ linguistic and semiotic resources as valid in the learning enterprise. It recommends the enactment of specific clauses on language in education policies and curriculum documents that empower classroom interactants to exercise discretion in languaging practices.

Keywords: languaging, monolingual, multilingual, semiotic and linguistic repertoire

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Authors: Bahareh Golchin, Nooshin Riahi

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One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: Elcin Timur Cakmak, Ayse Oguzlar

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This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

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Authors: Dora Triki, Irena Vida, Claude Obadia

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Social networks such as e-Forums or dating sites often face the reluctance of key members to participate. Relying on the conation theory, this study investigates this phenomenon and proposes solutions to mitigate the issue. We show that highly experienced e-Forum members refuse to share business information in a peer to peer information exchange forums. However, forums managers can mitigate this behavior by developing a sentiment of belongingness to the network. Furthermore, by selecting only elite forum participants with ample experience, they can reduce the reluctance of key information providers to engage in information exchange. Our hypotheses are tested with PLS structural equations modeling using survey data from members of a French e-Forum dedicated to the exchange of business information about exporting.

Keywords: conation, e-Forum, information exchange, members participation

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Authors: Mzamani Aaron Mabasa

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The research study examines the reality that the Language Services Directorate can be considered a language repository hub. The study postulates that multilingual education guided by language policy implementation can improve student performance and pass rate. Various documents in the form of style guides, glossaries and tutorial letters may be used to enable students to understand complex words, sentences, phrases and paragraphs when technical vocabularies are used. This paper addresses the way in which quality assurance can transform South African official languages, including Sign Language, as mandated by the Language Policy for Higher Education. The paper further emphasizes that Language Services is unique in the sense that it involves all South African officials as tools for student support and facilitation of learning. This is in line with the Constitution of the Republic of South Africa (1996) and the Unisa Language Policy of 2023, which declares the status, parity and esteem of these official languages regarding usage in formal function domains, namely education, economy, social and politics. The aim of this paper is to ensure that quality assurance is ultimately accomplished in terms of teaching and learning standards. Eventually, all South African languages can be used for official domains to achieve functional multilingualism. This paper furthermore points out that content analysis as a research instrument as far as a qualitative approach is concerned may be used as a data collection technique.

Keywords: repository, multilingualism, policy, education

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Authors: Adeep Hande, Shubham Agarwal

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This paper presents a study on identifying sexism in online texts using various state-of-the-art deep learning models based on BERT. We experimented with different feature sets and model architectures and evaluated their performance using precision, recall, F1 score, and accuracy metrics. We also explored the use of pseudolabeling technique to improve model performance. Our experiments show that the best-performing models were based on BERT, and their multilingual model achieved an F1 score of 0.83. Furthermore, the use of pseudolabeling significantly improved the performance of the BERT-based models, with the best results achieved using the pseudolabeling technique. Our findings suggest that BERT-based models with pseudolabeling hold great promise for identifying sexism in online texts with high accuracy.

Keywords: large language models, semi-supervised learning, sexism detection, data sparsity

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Authors: Omar Alomoush

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This paper examines the Armenian language in the linguistic landscape of Jordanian cities. The results indicate that Armenian is chiefly marginalised in the LL. By quantitative and qualitative methods, the current study attempts to identify the main reasons behind this marginalisation. In the light of the fact that Armenian is completely absent from the commercial streets of major Jordanian cities, all monolingual and multilingual signs in Armenian Neighbourhood in Amman city are photographed to identify them according to function and language. To provide plausible explanations for the marginalisation of the Armenian language in the LL, the current study builds upon issues of language maintenance and underlying language policy. According to the UNESCO Endangerment Framework, it can be assumed that Armenian is a vulnerable language, even though the Armenian Church exerted great efforts to revitalise Armenian in all social settings, including the LL. It was found that language policies enacted by the state of Jordan, language shift, language hostility, voluntary migration and economic pressures are among the reasons behind this marginalisation.

Keywords: linguistic landscape, multilingualism, Armenian, marginalisation and revitalisation

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