Search results for: multilingual sentiment analysis

Authors: Bakhtiar Amen, Abduladim Ali, Joan Lu

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Recently, wireless technologies have made tremendous influences in advanced technology era, precisely on the learning environment through PADs and smart phones to engage learners to collaborate effectively. In fact, the wireless communication technologies are widely adopted in the education sectors within most of the countries to deliver education support electronically. Today, Introducing multilingual Wireless Response System (WRS) application is an enormous challenge and complex. The purpose of this paper is to implementing internationalization testing strategy through WRS application case study and proposed a questionnaire in multilingual speakers like (Arabic, Kurdish, Chines, Malaysian, Turkish, Dutch, Polish, Russian) to measure the internationalization testing results which includes localization and cultural testing results. This paper identifies issues with each language’s specification attributes for instance right to left (RTL) screen direction related languages, Linguistic test or word spaces in Chines and Dutch languages. Finally, this paper attempt to emphasizes many challenges and solutions that associated with globalization testing model.

Keywords: mobile WRS, internationalization, globalization testing

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Authors: Yahya Almurtadha, Mukhtar Ghaleb, Ahmed M. Shamsan Saleh

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The world witnessed a fierce attack from the Covid-19 virus, which affected public life socially, economically, healthily and psychologically. The world's governments tried to confront the pandemic by imposing a number of precautionary measures such as general closure, curfews and social distancing. Scientists have also made strenuous efforts to develop an effective vaccine to train the immune system to develop antibodies to combat the virus, thus reducing its symptoms and limiting its spread. Artificial intelligence, along with researchers and medical authorities, has accelerated the vaccine development process through big data processing and simulation. On the other hand, one of the most important negatives of the impact of Covid 19 was the state of anxiety and fear due to the blowout of rumors through social media, which prompted governments to try to reassure the public with the available means. This study aims to proposed using Sentiment Analysis (AKA Opinion Mining) and deep learning as efficient artificial intelligence techniques to work on retrieving the tweets of the public from Twitter and then analyze it automatically to extract their opinions, expression and feelings, negatively or positively, about the symptoms they may feel after vaccination. Sentiment analysis is characterized by its ability to access what the public post in social media within a record time and at a lower cost than traditional means such as questionnaires and interviews, not to mention the accuracy of the information as it comes from what the public expresses voluntarily.

Keywords: deep learning, opinion mining, natural language processing, sentiment analysis

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Elisa Santana-Monagas, Juan L. Núñez, Jaime León, Samuel Falcón, Celia Fernández, Rocío P. Solís

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Teaching practices have been widely studied in relation to students' outcomes, positioning themselves as one of their strongest catalysts and influencing students' emotional experiences. In the higher education context, teachers become even more crucial as many students ground their decisions on which courses to enroll in based on opinions and ratings of teachers from other students. Unfortunately, sometimes universities do not provide the personal, social, and academic stimulation students demand to be actively engaged. To evaluate their teachers, universities often rely on students' evaluations of teaching (SET) collected via Likert scale surveys. Despite its usefulness, such a method has been questioned in terms of validity and reliability. Alternatively, researchers can rely on qualitative answers to open-ended questions. However, the unstructured nature of the answers and a large amount of information obtained requires an overwhelming amount of work. The present work presents an alternative approach to analyse such data: sentiment analysis. To the best of our knowledge, no research before has included results from SA into an explanatory model to test how students' sentiments affect their emotional engagement in class. The sample of the present study included a total of 225 university students (Mean age = 26.16, SD = 7.4, 78.7 % women) from the Educational Sciences faculty of a public university in Spain. Data collection took place during the academic year 2021-2022. Students accessed an online questionnaire using a QR code. They were asked to answer the following open-ended question: "If you had to explain to a peer who doesn't know your teacher how he or she communicates in class, what would you tell them?". Sentiment analysis was performed using Microsoft's pre-trained model. The reliability of the measure was estimated between the tool and one of the researchers who coded all answers independently. The Cohen's kappa and the average pairwise percent agreement were estimated with ReCal2. Cohen's kappa was .68, and the agreement reached was 90.8%, both considered satisfactory. To test the hypothesis relations among SA and students' emotional engagement, a structural equation model (SEM) was estimated. Results demonstrated a good fit of the data: RMSEA = .04, SRMR = .03, TLI = .99, CFI = .99. Specifically, the results showed that student’s sentiment regarding their teachers’ teaching positively predicted their emotional engagement (β == .16 [.02, -.30]). In other words, when students' opinion toward their instructors' teaching practices is positive, it is more likely for students to engage emotionally in the subject. Altogether, the results show a promising future for sentiment analysis techniques in the field of education. They suggest the usefulness of this tool when evaluating relations among teaching practices and student outcomes.

Keywords: sentiment analysis, students' evaluation of teaching, structural-equation modelling, emotional engagement

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Authors: Elisa Santana-Monagas, Juan L. Núñez, Jaime León, Samuel Falcón, Celia Fernández, Rocío P. Solís

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Students’ emotional experiences have been a widely discussed theme among researchers, proving a central role on students’ outcomes. Yet, up to now, far too little attention has been paid to teaching practices that negatively relate with students’ negative emotions in the higher education. The present work aims to examine the relationship between teachers’ teaching practices (i.e., students’ evaluations of teaching and autonomy support), the students’ feelings of boredom and agentic engagement and motivation in the higher education context. To do so, the present study incorporates one of the most popular tools in natural processing language to address students’ evaluations of teaching: sentiment analysis. Whereas most research has focused on the creation of SA models and assessing students’ satisfaction regarding teachers and courses to the author’s best knowledge, no research before has included results from SA into an explanatory model. A total of 225 university students (Mean age = 26.16, SD = 7.4, 78.7 % women) participated in the study. Students were enrolled in degree and masters’ studies at the faculty of Education of a public university of Spain. Data was collected using an online questionnaire students could access through a QR code they completed during a teaching period where the assessed teacher was not present. To assess students’ sentiments towards their teachers’ teaching, we asked them the following open-ended question: “If you had to explain a peer who doesn't know your teacher how he or she communicates in class, what would you tell them?”. Sentiment analysis was performed with Microsoft's pre-trained model. For this study, we relied on the probability of the students answer belonging to the negative category. To assess the reliability of the measure, inter-rater agreement between this NLP tool and one of the researchers, who independently coded all answers, was examined. The average pairwise percent agreement and the Cohen’s kappa were calculated with ReCal2. The agreement reached was of 90.8% and Cohen’s kappa .68, both considered satisfactory. To test the hypothesis relations a structural equation model (SEM) was estimated. Results showed that the model fit indices displayed a good fit to the data; χ² (134) = 351.129, p < .001, RMSEA = .07, SRMR = .09, TLI = .91, CFI = .92. Specifically, results show that boredom was negatively predicted by autonomy support practices (β = -.47[-.61, -.33]), whereas for the negative sentiment extracted from SET, this relation was positive (β = .23[.16, .30]). In other words, when students’ opinion towards their instructors’ teaching practices was negative, it was more likely for them to feel bored. Regarding the relations among boredom and student outcomes, results showed a negative predictive value of boredom on students’ motivation to study (β = -.46[-.63, -.29]) and agentic engagement (β = -.24[-.33, -.15]). Altogether, results show a promising future for sentiment analysis techniques in the field of education as they proved the usefulness of this tool when evaluating relations among teaching practices and student outcomes.

Keywords: sentiment analysis, boredom, motivation, agentic engagement

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Authors: Marina Petrova

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Language policy issues related to the preservation and development of the native languages of the Russian peoples and the state languages of the national republics are increasingly becoming the focus of recent attention of educators and parents, public and national figures. Is it legal to teach the national language or the mother tongue as the state language? Due to that dispute language phobia moods easily evolve into xenophobia among the population. However, a civilized, intelligent multicultural personality can only be formed if the country develops bilingualism and multilingualism, and languages as a political tool help to find ‘keys’ to sufficiently closed national communities both within a poly-ethnic state and in internal relations of multilingual countries. The purpose of this study is to design and theoretically substantiate an efficient model of language education in the innovatively developing Republic of Sakha. 800 participants from different educational institutions of Yakutia worked at developing a multilingual model of education. This investigation is of considerable practical importance because researchers could build a methodical system designed to create conditions for the formation of a cultural language personality and the development of the multilingual communicative competence of Yakut youth, necessary for communication in native, Russian and foreign languages. The selected methodology of humane-personal and competence approaches is reliable and valid. Researchers used a variety of sources of information, including access to related scientific fields (philosophy of education, sociology, humane and social pedagogy, psychology, effective psychotherapy, methods of teaching Russian, psycholinguistics, socio-cultural education, ethnoculturology, ethnopsychology). Of special note is the application of theoretical and empirical research methods, a combination of academic analysis of the problem and experienced training, positive results of experimental work, representative series, correct processing and statistical reliability of the obtained data. It ensures the validity of the investigation’s findings as well as their broad introduction into practice of life-long language education.

Keywords: intercultural communication, language policy, multilingual and multicultural education, the Sakha Republic of Yakutia

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Authors: Mohsen Maraoui

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In the aim of natural language processing applications improvement, such as information retrieval, machine translation, lexical disambiguation, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. These concepts represent the content of the document. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource Euro WordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules model (in attempt to discover the non-taxonomic relations or contextual relations between the concepts of a document). These relations are latent relations buried in the text and carried by the semantic context of the co-occurrence of concepts in the document. Our proposed indexing approach can be applied to text documents in various languages because it is based on a linguistic method adapted to the language through a multilingual thesaurus. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results.

Keywords: concept extraction, conceptual network formalism, fuzzy association rules, multilingual thesaurus, semantic indexing

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Authors: Marta Bas-Szymaszek

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This research examines the intricate relationship between language practices and beliefs within international environmental non-governmental organizations (ENGOs), with a particular focus on the Climate Action Network Europe (CAN Europe). While acknowledging that ENGOs often employ multilingual staff, this study aims to analyze the dual role of English within this sector. While English facilitates practical communication among individuals from diverse backgrounds, it also perpetuates inequalities and marginalization within CAN Europe. Instances of linguistic dominance impede participation and representation, reinforcing language hierarchies. Furthermore, the symbolic power of English risks overshadowing the multilingual skills of NGO employees. Through fourteen in-depth interviews, focus group discussions, and observations, this research uncovers the lived experiences of individuals navigating Europe’s largest environmental NGO network. By analyzing CAN Europe’s implicit language policy and the hegemony of English, this study illuminates the challenges within multilingual settings. The organization advocates for the implementation of more inclusive language policies and practices, with the objective of recognizing and embracing linguistic diversity within international environmental NGOs.

Keywords: language policy, English, NGOs, linguistic inclusion, multilingualism

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Authors: Cindy Agyemang

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This study seeks to find out if racial sentiment toward immigrants still matters in the United States with COVID-19 present. It is argued that previous studies on immigration and racial attitudes or race conducted do not consider how health-related pandemics influence public opinion on immigration and the racial attitudes of people during severe health-related pandemics. In doing so, this paper hypothesizes that respondents' racial sentiment towards immigrants during this pandemic will influence their views on opposing immigration, those that believe the president handled cases on COVID-19 better are more likely to oppose immigration, and party affiliation affects respondents' views on immigration and COVID-19. For testing these hypotheses, the 2012, 2016, and 2020 American National Election Studies data was used. In accordance with the expectations of this study, it was observed that there was a statistically significant relationship between all my estimated models. This paper concludes that racial sentiment toward immigrants still matters even more in the United States, especially with the existence of health-related pandemics.

Keywords: COVID-19, immigration, racial attitudes, partisanship

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Authors: Amin Kaveh Boukani

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Foreign language learning is not straightforward and can be affected by numerous factors, among which personality features like tolerance of ambiguity (TA) are so well-known and important. Such characteristics yet can be affected by other factors like learning additional languages. The current investigation, thus, opted to explore the possible effect of linguistic background (being bilingual or trilingual) on the tolerance of ambiguity (TA) of Iranian EFL learners. Furthermore, the possible mediating effect of TA on multilingual learners' language performance (listening comprehension in this study) was expounded. This research involved 68 EFL learners (32 bilinguals, 29 trilinguals) with the age range of 19-29 doing their degrees in the Department of English Language and Literature of Urmia University. A set of questionnaires, including tolerance of ambiguity (Herman et. al., 2010) and linguistic background information (Modirkhameneh, 2005), as well as the IELTS listening comprehension test, were used for data collection purposes. The results of a set of independent samples t-test and mediation analysis (Hayes, 2022) showed that (1) linguistic background (being bilingual or trilingual) had a significant direct effect on EFL learners' TA, (2) Linguistic background had a significant direct influence on listening comprehension, (3) TA had a substantial direct influence on listening comprehension, and (4) TA moderated the influence of linguistic background on listening comprehension considerably. These results suggest that multilingualism may be considered as an advantageous asset for EFL learners and should be a prioritized characteristic in EFL instruction in multilingual contexts. Further pedagogical implications and suggestions for research are proposed in light of effective EFL instruction in multilingual contexts.

Keywords: tolerance of ambiguity, listening comprehension, multilingualism, bilingual, trilingual

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Authors: Derek Brandon G. Yu, Clyde Vincent O. Pilapil, Christine F. Peña

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College students of Cebu province have been indoors since March 2020, and a challenge encountered is the sudden shift from face to face to online learning and with the lack of empirical data on online learning on Higher Education Institutions (HEIs) in the Philippines. Sentiments on face to face and online learning will be collected from University of San Carlos (USC), School of Arts and Sciences (SAS) students regarding Mathematics in the Modern World (MMW), a General Education (GE) course. Natural Language Processing with machine learning algorithms will be used to classify the sentiments of the students. Results of the research study are the themes identified through topic modelling and the overall sentiments of the students in USC SAS

Keywords: natural language processing, online learning, sentiment analysis, topic modelling

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Authors: M. A. T. AlSudairi, T. G. K. Vasista

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The concept of social media is becoming the top of the agenda for many business executives and public sector executives today. Decision makers as well as consultants, try to identify ways in which firms and enterprises can make profitable use of social media and network related applications such as Wikipedia, Face book, YouTube, Google+, Twitter. While it is fun and useful to participating in this media and network for achieving the communication effectively and efficiently, semantic and sentiment analysis and interpretation becomes a crucial issue. So, the objective of this paper is to provide literature review on social media, network and related technology related to semantics and sentiment or opinion analysis covering business and governance perspectives. In this regard, a case study on the use and adoption of Social media in Saudi Arabia has been discussed. It is concluded that semantic web technology play a significant role in analyzing the social networks and social media content for extracting the interpretational knowledge towards strategic decision support.

Keywords: CRASP methodology, formative assessment, literature review, semantic web services, social media, social networks

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Authors: Xiuying Wang, Changliang Li, Bo Xu

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A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

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Authors: E. Machinyise

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The main purpose of this study was to investigate the attitudes of parents and teachers towards multilingual medium of instruction in lower primary schools of Zambia. In 2013, the Government of Zambia formulated a language policy which stipulates that regional familiar languages should be used as the medium of instruction (MOI) from grade one to four in all public primary schools, while English is introduced as a subject in the second grade. This study investigated the views of parents and teachers on the use of multilingual medium of instruction in lower primary schools in order to accommodate learners who are not native speakers of regional familiar languages as well as the second languages which are official languages used in class. The study revealed that most parents suggested that teachers who teach lower primary school classes should be conversant with at least the four major local languages of Zambia (Bemba, Nyanja, Tonga and Lozi). In the same vain other parents felt that teachers teaching lower grades should not only be familiar with the regional official language but should be able to speak other dialects found in the region. Teachers teaching in lower primary grade felt that although it is difficult to speak all languages of learners in class, it is important for a teacher of lower grade class to try to accommodate children who are not speakers of the familiar languages by addressing them in the language they understand. Both teachers and parents highlighted a number of advantages of teaching children in their mother tongues. Both qualitative and quantitative methods were used for the collection of data for this study. 30 teachers from selected public primary schools and 20 parents of Kapiri district and five lecturers of teacher training colleges in Central province were selected for this study. The researcher also observed class lessons in lower primary schools of Kapiri district. This study revealed that both parents and teachers are of the views that teachers teaching lower primary classes should use multilingual medium of instruction in lower primary classes so as to accommodated children of different linguistic backgrounds.

Keywords: familiar languages, medium of instruction, multilingual medium of instruction, native speakers

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Authors: G. C. Joshi, M. Paul, B. K. Kalita, V. Ranga, J. S. Rawat, P. S. Rawat

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Being a habitat of the global village, every place has established connection through the strength and power of social media piercing through the political boundaries. Social media is a digital platform, where people across the world can interact as it has advantages of being universal, anonymous, easily accessible, indirect interaction, gathering and sharing information. The power of social media lies in the intensity of sharing extreme opinions or feelings, in contrast to the personal interactions which can be easily mapped in the form of Sentiment Mapping. The easy access to social networking sites such as Facebook, Twitter and blogs made unprecedented opportunities for citizens to voice their opinions loaded with dynamics of emotions. These further influence human thoughts where social media plays a very active role. A recent incident of public importance was selected as a case study to map the sentiments of people through Twitter. Understanding those dynamics through the eye of an ordinary people can be challenging. With the help of R-programming language and by the aid of GIS techniques sentiment maps has been produced. The emotions flowing worldwide in the form of tweets were extracted and analyzed. The number of tweets had diminished by 91 % from 25/08/2017 to 31/08/2017. A boom of sentiments emerged near the origin of the case, i.e., Delhi, Haryana and Punjab and the capital showed maximum influence resulting in spillover effect near Delhi. The trend of sentiments was prevailing more as neutral (45.37%), negative (28.6%) and positive (21.6%) after calculating the sentiment scores of the tweets. The result can be used to know the spatial distribution of digital penetration in India, where highest concentration lies in Mumbai and lowest in North East India and Jammu and Kashmir.

Keywords: sentiment mapping, digital literacy, GIS, R statistical language, spatio-temporal

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Authors: Zhi Hao Chow, Cian Mulligan, Jack Walsh, Antonio Garzon Vico, Dimitar Krastev

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Drug development is resource-intensive, time-consuming, and increasingly expensive with each developmental stage. The success rates of drug development are also relatively low, and the resources committed are wasted with each failed candidate. As such, a reliable method of predicting the success of drug development is in demand. The hypothesis was that some examples of failed drug candidates are pushed through developmental pipelines based on false confidence and may possess common linguistic features identifiable through sentiment analysis. Here, the concept of using text analysis to discover such features in research publications and investor reports as predictors of success was explored. R studios were used to perform text mining and lexicon-based sentiment analysis to identify affective phrases and determine their frequency in each document, then using SPSS to determine the relationship between our defined variables and the accuracy of predicting outcomes. A total of 161 publications were collected and categorised into 4 groups: (i) Cancer treatment, (ii) Neurodegenerative disease treatment, (iii) Vaccines, and (iv) Others (containing all other drugs that do not fit into the 3 categories). Text analysis was then performed on each document using 2 separate datasets (BING and AFINN) in R within the category of drugs to determine the frequency of positive or negative phrases in each document. A relative positivity and negativity value were then calculated by dividing the frequency of phrases with the word count of each document. Regression analysis was then performed with SPSS statistical software on each dataset (values from using BING or AFINN dataset during text analysis) using a random selection of 61 documents to construct a model. The remaining documents were then used to determine the predictive power of the models. Model constructed from BING predicts the outcome of drug performance in clinical trials with an overall percentage of 65.3%. AFINN model had a lower accuracy at predicting outcomes compared to the BING model at 62.5% but was not effective at predicting the failure of drugs in clinical trials. Overall, the study did not show significant efficacy of the model at predicting outcomes of drugs in development. Many improvements may need to be made to later iterations of the model to sufficiently increase the accuracy.

Keywords: data analysis, drug development, sentiment analysis, text-mining

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Authors: Luyao Zhang, Tianyu Wu, Jiayi Li, Carlos-Gustavo Salas-Flores, Saad Lahrichi

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Recent advances in AI have made algorithmic trading a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating algorithmic trading of stock and crypto tokens. Moreover, we provide comparative case studies for four conventional algorithms, including moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage. Our study offers a systematic way to program and compare different trading strategies. Moreover, we implement our algorithms by object-oriented programming in Python3, which serves as open-source software for future academic research and applications.

Keywords: algorithmic trading, AI for finance, fintech, machine learning, moving average crossover, volume weighted average price, sentiment analysis, statistical arbitrage, pair trading, object-oriented programming, python3

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Authors: Anupama Purohit

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The paper is a study on the inevitable cross-linguistic effect found in the early multilingual learners. The cross-linguistic behaviour like code-mixing, code-switching, foreign accent, literal translation, redundancy and syntactic manipulation effected due to other languages on the English language output of a non-native pre-school child are discussed here. A case study method is adopted in this paper to support the claim of the title. A simultaneously tetra lingual pre-school child’s (within 1;3 to 4;0) language behaviour is analysed here. The sample output data of the child is gathered from the diary entries maintained by her family, regular observations and video recordings done since her birth. She is getting the input of her mother tongue, Sambalpuri, from her grandparents only; Hindi, the local language from her play-school and the neighbourhood; English only from her mother and occasional visit of other family friends; Odia only during the reading of the Odia story book. The child is exposed to code-mixing of all the languages throughout her childhood. But code-mixing, literal translation, redundancy and duplication were absent in her initial stage of multilingual acquisition. As the child was more proficient in English in comparison to her other first languages and had never heard code-mixing in English language; it was expected from her input pattern of English (one parent, English language) that she would maintain purity in her use of English while talking to the English language interlocutor. But with gradual increase in the language proficiency in each of the languages of the child, her handling of the multiple codes becomes deft cross-linguistically. It can be deduced from the case study that after attaining certain milestone proficiency in each language, the child’s linguistic faculty can operate at a metalinguistic level. The functional use of each morpheme, their arrangement in words and in the sentences, the supra segmental features, lexical-semantic mapping, culture specific use of a language and the pragmatic skills converge to give a typical childlike multilingual output in an intelligible manner to the multilingual people (with the same set of languages in combination). The result is appealing because for expressing the same ideas which the child used to speak (may be with grammatically wrong expressions) in one language, gradually, she starts showing cross-linguistic effect in her expressions. So the paper pleads for the separatist view from the very beginning of the holophrastic phase (as the child expresses in addressee-specific language); but development of a metalinguistic ability that helps the child in communicating in a sophisticated way according to the linguistic status of the addressee is unique to the multilingual child. This metalinguistic ability is independent of the mode if input of a multilingual child.

Keywords: code-mixing, cross-linguistic effect, early multilingualism, literal translation

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Authors: Peter Ecke

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This research presents insights from a questionnaire study and autobiographic narrative analyses about the language and cultural backgrounds, challenges, interests, and needs, as well as perceptions about bilingualism and language learning of undergraduate students at a Public University in the southwestern United States. Participants were 650 students, enrolled in college-level general education courses, entitled “Becoming multilingual: Learning and maintaining two or more languages” between 2020 and 2024. Data were collected via pre- and post-course questionnaires administered online through the Qualtrix XM platform and complemented with analyses of excerpts from autobiographical narratives that students produced as part of the course assignments. Findings, for example, show that course participants have diverse linguistic backgrounds. The five most frequently reported L1s were English (about 50% of course participants), Spanish, Arabic, Mandarin, and Korean (in that order). The five most frequently reported L2s were English, Spanish, French, ASL, Japanese, German, and Mandarin (in that order). Participants also reported on their L2, L3, L4, and L5 if applicable. Most participants (over 60%) rated themselves bilingual or multilingual whereas 40% considered themselves to be monolingual or foreign language learners. Only about half of the participants reported feeling very or somewhat comfortable with their language skills, but these reports changed somewhat from the pre- to the post-course survey. About half of participants were mostly interested in learning how to effectively learn a foreign language. The other half of participants reported being most curious about learning about themselves as bi/multilinguals, (re)learning a language used in childhood, learning how to bring up a child as a bi/multilingual or learning about people who speak multiple languages (distributed about evenly). Participants’ comments about advantages and disadvantages of being bilingual remained relatively stable but their agreement with common myths about bilingualism and language learning changed from the pre- to the post-course survey. Students’ reflections in the autobiographical narratives and comments in (institutionally administered) anonymous course evaluations provided additional data on students’ concerns about their current language skills and uses as well as their perceptions about learning outcomes and the usefulness of the general education course for their current and future lives. It is hoped that the presented findings and discussion will spark interest among colleagues in offering similar courses as a resource for college students (and possibly other audiences), including those from migrant, indigenous, multilingual, and multicultural communities to contribute to a more harmonious bilingualism and well-being of college students who are or inspire to become bi-or multilingual.

Keywords: autobiographic narratives, general education university course, harmonious bilingualism and well-being, multilingualism, questionnaire study

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

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Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

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Authors: Oussama Hafferssas, Hiba Benyahia, Amina Madani, Nassima Zeriri

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Although data nowadays has multiple forms; from text to images, and from audio to videos, yet text is still the most used one at a public level. At an academical and research level, and unlike other forms, text can be considered as the easiest form to process. Therefore, a brunch of Data Mining researches has been always under its shadow, called "Text Mining". Its concept is just like data mining’s, finding valuable patterns in data, from large collections and tremendous volumes of data, in this case: Text. Named entity recognition (NER) is one of Text Mining’s disciplines, it aims to extract and classify references such as proper names, locations, expressions of time and dates, organizations and more in a given text. Our approach "Octopub" does not aim to find new ways to improve named entity recognition process, rather than that it’s about finding a new, and yet smart way, to use NER in a way that we can extract sentiments of millions of people using Social Networks as a limitless information source, and Marketing for product promotion as the main domain of application.

Keywords: textmining, named entity recognition(NER), sentiment analysis, social media networks (SN, SMN), business intelligence(BI), marketing

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Authors: Luzia Dominguez

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This paper explores the concept of ‘translanguaging’ and the relevance of its pedagogical application in the context of foreign language education in a multilingual urban environment. We review relevant literature discussing this theoretical concept, its scope, potential, and limitations when applied to the teaching of foreign languages. We then discuss its possible practical application in Welsh secondary schools, particularly in the most diverse areas of the Welsh capital, Cardiff (United Kingdom). The concept of translanguaging has evolved in scope, from its initial application in the teaching of Welsh and English in the Welsh bilingual context to finding a relevant space not only in the international arena of Sociolinguistics and language pedagogy but also being present in current Welsh educational policies and, presumably, practices. However, it is important to consider the actual pedagogical relevance of incorporating this concept into these policies, particularly in the teaching of Modern Foreign Languages. Additionally, it is important to examine any social factors that may influence the effectiveness of its application in the social context, in our case, a multilingual, ethnically diverse urban context. By analyzing these issues, we aim to explore possible teaching practices that could be pedagogically effective in applying the concept in Cardiff secondary schools.

Keywords: pedagogy, modern foreign languages, applied linguistics, sociolinguistics

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Authors: Olivia Martina Dalla Torre

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The aim of this paper is to describe and analyse some urban writings that emerge in politically disputed areas, namely the Occupied Palestinian Territories, and more specifically in Deheishe refugee camp. These graffiti are visible on the walls of houses, all around the camp, and they convey messages of protest but also of hope or claim about the complex political situation in the occupied territories. These graffiti can be then interpreted as political and politicized semiotic resources. In this paper, after having introduced the political situation of the Palestinian Territories in a historical perspective, we will question a specific dimension of these writings, i.e., their multilingual and ideological aspect. To do this, we will focus on ethnographic fieldwork on Deheishe refugee camp and we will draw on the theoretical framework of the critical communication studies which assert that language practices are not neutral and that they need to be understood through the lens of the historical context of production, crossing space and time. By analysing the relationship between the discursive constructions of the messages and the languages used, we will point out some of the possible reasons and functions of the presence of these multilingual discursive productions. We will show that if, on the one hand, these graffiti confirm the huge presence of Western actors in the region, on the other hand, they attest the presence of an international movement against the Israeli occupation and against other struggles as well. Concluding, we will argue that multilingualism certainly represents a diversification of the linguistic landscape and that it gives a transnational and political dimension to the graffiti.

Keywords: communication, graffiti, multilingualism, Palestine, transnationalism

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Authors: Jummalla Aashrith, N. L. Shiva Sai, K. Bhavya Sri

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The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter.

Keywords: machine learning, sentiment analysis, visualisation, python

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Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

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The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: Sterling C. DeVerter

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In recent decades, there has been continually growing concern amongst scholars and political leaders towards the global resurgence of nationalism, particularly in Europe, the United States, and China. However, very few studies have attempted to empirically examine the relationship between religion and nationalism at the level of the individual, and none are known to have done so quantitatively. Building on Tajfel's and Turner's (1978) Social Identity Theory (SIT), and Anderson (1991) and Marx (2003), this study will employ SIT and regression analysis to compare the sources and patterns of nationalistic sentiment among European respondents in eight countries to the average levels of self-reported religiosity, religious participation, age, education, and income levels. Survey reports from the International Social Survey Programme were the primary quantitative data sources. It was hypothesized that the increase in nationalism across Europe follows this same evolution as first identified by Anderson, and is positively correlated to the reduction in reported religiosity. However, this study failed to reject the null, there was no substantial ( < .035) correlation between nationalistic sentiment and any of the measures of religiosity, nor were there any substantial correlations between nationalistic sentiment and either of the three control variables ( < .008). Across all countries examined, it was discovered that inclusionary nationalism has slightly declined (-5.08%), while exclusionary nationalism had increased substantially (+17.25%). The combined trend reflected an overall rise in nationalism across the time period and a forecast that suggests the current levels are also elevated. The primary implications include the demand to readdress the notion of religion and nationalism, and the correlation between the two, as well as the current nationalism trends in terms of support or non-support for future political and social movements.

Keywords: European Union, secularization, nationalism, social identity theory

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Authors: Anya Glikman, Anastasia Gorodzeisky

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Since the late 1990th labor immigration and its consequences on the society have become one of the most frequently discussed and debated issues in Russia. Social scientists point that the negative attitudes towards immigrants among Russian majority population is widespread, and their level, at least, twice as high as their level in most other European countries. Moreover, recent study by Gorodzeisky, Glikman and Maskyleison (2014) demonstrates that the two sets of individual level predictors of anti-foreigner sentiment – socio-economic status and conservative views and ideologies – that have been repeatedly proved in research in Western countries are not effective in predicting of anti-foreigner sentiment in Post-Socialist Russia. Apparently, the social mechanisms underlying anti-foreigner sentiment in Western countries, which are characterized by stable regimes and relatively long immigration histories, do not play a significant role in the explanation of anti-foreigner sentiment in Post-Socialist Russia. The present study aims to examine alternative possible sources of anti-foreigner sentiment in Russia while controlling for socio-economic position of individuals and conservative views. More specifically, following the research literature on the topic worldwide, we aim to examine whether and to what extent human values (such as tradition, universalism, safety and power), ethnic residential segregation, fear of crime and exposure to mass media affect anti-foreigner sentiments in Russia. To do so, we estimate a series of multivariate regression equations using the data obtained from 2012 European Social Survey. The national representative sample consists of 2337 Russian born respondents. Descriptive results reveal that about 60% percent of Russians view the impact of immigrants on the country in negative terms. Further preliminary analysis show that anti-foreigner sentiments are associated with exposer to mass media as well as with fear of crime. Specifically, respondents who devoted more time watching news on TV channels and respondents who express higher levels of fear of crime tend to report higher levels of anti-immigrants sentiments. The findings would be discussed in light of sociological perspective and the context of Russian society.

Keywords: anti-immigrant sentiments, fear of crime, human values, mass media, Russia

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Authors: Manoj Gujar

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The present paper is an attempt to interpret two Marathi translations of Khushwant Singh’s (1915-2014) novel Train to Pakistan (1956). The 20th century was branded as an era of Liberalization, Privatization and Globalization. Different countries and cultures have enunciated interaction with one another in an unprecedented manner. The world is becoming multilingual and multicultural. The democratic countries such as the U.S.A., the U.K., and India have become pivotal centers of interlingual and cross-cultural exchange. People belonging to different nationalities showed keen interest in knowing the characteristic features of different languages and of their cultures. Here, ‘Translation’ plays an important role in such multilingual and multicultural contexts. Translation is not only translation of a language but a translation of a culture. However, in the act of translation a translator makes use of such procedures as borrowing, definition, literal translation, substitution, lexical creation, omission, addition as well as their various combinations. To him, a text produced in one linguistic and cultural context can reach other linguistic and cultural contexts through these processes of translation. A worthy work of art appeals many readers. India, being a multilingual country we find that there goes multiple translations of the same text in different Indian languages. But sometimes, if can be found that a same text appeals to different ages and the same text gets translated into the same language by the two or more authors. In this reference, the present paper is an attempt to study how different translations of the same text differ in terms of procedures and strategies during the process of the translation of culture. The source text is Khushwant Singh’s historical novel Train to Pakistan (1956). The novel was widely appreciated and so translated into different regional languages in India. The novel has two Marathi translations: Agniratha (1972) by Hidayatkhan and Train to Pakistan (1980) by Anil Kinikar. This paper is an attempt to evaluate the strategies and procedures in translation to analyze these two Marathi translations. Hidayat Khan made a lot of omissions of the significant details and distorted the original text to a large extent, whereas, Anil Kinikar has done justice to the Source Text by rendering it in Marathi as faithfully as possible.

Keywords: culture, multilingual, procedures and strategies, translation

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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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