Search results for: multilingual sentiment analysis

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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: Kim Dongwon, Yu Songjin

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This study identifies the business value of VOC (Voice of Customer) on the business. Precisely, we intend to demonstrate how much negative and positive sentiment of VOC has an influence on car sales market share in the unites states. We extract 7 emotions such as sadness, shame, anger, fear, frustration, delight and satisfaction from the VOC data, 23,204 pieces of opinions, that had been posted on car-related on-line community from 2007 to 2009(a part of data collection from 2007 to 2015), and intend to clarify the correlation between negative and positive sentimental keywords and contribution to market share. In order to develop a lexicon for each category of negative and positive sentiment, we took advantage of Corpus program, Antconc 3.4.1.w and on-line sentimental data, SentiWordNet and identified the part of speech(POS) information of words in the customers' opinion by using a part-of-speech tagging function provided by TextAnalysisOnline. For the purpose of this present study, a total of 45,741 pieces of customers' opinions of 28 car manufacturing companies had been collected including titles and status information. We conducted an experiment to examine whether the inclusion, frequency and intensity of terms with negative and positive emotions in each category affect the adoption of customer opinions for vehicle organizations' market share. In the experiment, we statistically verified that there is correlation between customer ideas containing negative and positive emotions and variation of marker share. Particularly, "Anger," a domain of negative domains, is significantly influential to car sales market share. The domain "Delight" and "Satisfaction" increased in proportion to growth of market share.

Keywords: data mining, opinion mining, sentiment analysis, VOC

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Authors: Md. Rafiul Biswas, Uzair Shah, Mohammad Alkayal, Zubair Shah, Othman Althawadi, Kamila Swart

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Background: Social media platforms play a significant role in the mediated consumption of sport, especially so for sport mega-event. The characteristics of Twitter data (e.g., user mentions, retweets, likes, #hashtag) accumulate the users in one ground and spread information widely and quickly. Analysis of Twitter data can reflect the public attitudes, behavior, and sentiment toward a specific event on a larger scale than traditional surveys. Qatar is going to be the first Arab country to host the mega sports event FIFA World Cup 2022 (Q22). Qatar has hosted the FIFA Arab Cup 2021 (FAC21) to serve as a preparation for the mega-event. Objectives: This study investigates public sentiments and experiences about FAC21 and provides an insight to enhance the public experiences for the upcoming Q22. Method: FCA21-related tweets were downloaded using Twitter Academic research API between 01 October 2021 to 18 February 2022. Tweets were divided into three different periods: before T1 (01 Oct 2021 to 29 Nov 2021), during T2 (30 Nov 2021 -18 Dec 2021), and after the FAC21 T3 (19 Dec 2021-18 Feb 2022). The collected tweets were preprocessed in several steps to prepare for analysis; (1) removed duplicate and retweets, (2) removed emojis, punctuation, and stop words (3) normalized tweets using word lemmatization. Then, rule-based classification was applied to remove irrelevant tweets. Next, the twitter-XLM-roBERTa-base model from Huggingface was applied to identify the sentiment in the tweets. Further, state-of-the-art BertTopic modeling will be applied to identify trending topics over different periods. Results: We downloaded 8,669,875 Tweets posted by 2728220 unique users in different languages. Of those, 819,813 unique English tweets were selected in this study. After splitting into three periods, 541630, 138876, and 139307 were from T1, T2, and T3, respectively. Most of the sentiments were neutral, around 60% in different periods. However, the rate of negative sentiment (23%) was high compared to positive sentiment (18%). The analysis indicates negative concerns about FAC21. Therefore, we will apply BerTopic to identify public concerns. This study will permit the investigation of people’s expectations before FAC21 (e.g., stadium, transportation, accommodation, visa, tickets, travel, and other facilities) and ascertain whether these were met. Moreover, it will highlight public expectations and concerns. The findings of this study can assist the event organizers in enhancing implementation plans for Q22. Furthermore, this study can support policymakers with aligning strategies and plans to leverage outstanding outcomes.

Keywords: FIFA Arab Cup, FIFA, Twitter, machine learning

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Authors: Muthukumarasamy Govindarajan

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Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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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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Authors: Samer Muthana Sarsam, Abdul Samad Shibghatullah, Chit Su Mon, Abd Aziz Alias, Hosam Al-Samarraie

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Generative Artificial Intelligence chatbots (GAI chatbots) have emerged as promising tools in various domains, including higher education. However, their specific role within the educational context and the level of legal support for their implementation remain unclear. Therefore, this study aims to investigate the role of Bard, a newly developed GAI chatbot, in higher education. To achieve this objective, English tweets were collected from Twitter's free streaming Application Programming Interface (API). The Latent Dirichlet Allocation (LDA) algorithm was applied to extract latent topics from the collected tweets. User sentiments, including disgust, surprise, sadness, anger, fear, joy, anticipation, and trust, as well as positive and negative sentiments, were extracted using the NRC Affect Intensity Lexicon and SentiStrength tools. This study explored the benefits, challenges, and future implications of integrating GAI chatbots in higher education. The findings shed light on the potential power of such tools, exemplified by Bard, in enhancing the learning process and providing support to students throughout their educational journey.

Keywords: generative artificial intelligence chatbots, bard, higher education, topic modelling, sentiment analysis

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Authors: Duygu Dere, Mert Ergeneci, Kaan Gokcesu

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Google Trends data has gained increasing popularity in the applications of behavioral finance, decision science and risk management. Because of Google’s wide range of use, the Trends statistics provide significant information about the investor sentiment and intention, which can be used as decisive factors for corporate and risk management fields. However, an anomaly, a significant increase or decrease, in a certain query cannot be detected by the state of the art applications of computation due to the random baseline noise of the Trends data, which is modelled as an Additive white Gaussian noise (AWGN). Since through time, the baseline noise power shows a gradual change an adaptive thresholding method is required to track and learn the baseline noise for a correct classification. To this end, we introduce an online method to classify meaningful deviations in Google Trends data. Through extensive experiments, we demonstrate that our method can successfully classify various anomalies for plenty of different data.

Keywords: adaptive data processing, behavioral finance , convex optimization, online learning, soft minimum thresholding

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Authors: Dennis Infante, Leonora Yambao

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The Philippines is a multilingual community. A Filipino learns at least three languages throughout his lifespan. Since languages are learned and picked up simultaneously in the environment, a student naturally develops a language system that combines features of at least three languages: the local language, English and Filipino. This study seeks to investigate this particular phenomenon and aspires to propose a theoretical framework of unique language acquisition in the elementary, high school and college in the three languages spoken and used in media, community, business and school: Kapampangan, the local language; Filipino, the national language; and English. The study randomly selects five students from three participating schools in order to acquire language samples. The samples were analyzed in the subsentential, sentential and suprasentential levels using grammatical theories. The data are classified to map out the pattern of substitution or shifting from one language to another.

Keywords: language acquisition, mother tongue, multiculturalism, multilingual education

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Authors: Shunichiro Morishita

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This study examines the factors for attracting foreign guests in the Japanese lodging industry and discusses some measures taken for accepting foreign guests. It reviews three different accommodation providers acclaimed highly by foreign guests, Yamashiroya, Sawanoya and Fuji-Hakone Guest House, and identifies their characteristics. The common points for attracting foreign guests were: 1) making the best use of the old facilities, 2) multilingual signs, guidance and websites, 3) necessary and sufficient communication in English, 4) events and opportunities to experience Japanese culture, 5) omotenashi, warm and homely Japanese hospitality. These findings indicate that foreign guests’ dissatisfaction level can be decreased through internationalization utilizing ICT and by offering multilingual support. On the other hand, their satisfaction level can be increased by encouraging interaction with other guests and local Japanese people, providing events and opportunities to experience Japanese culture and omotenashi, home-style Japanese hospitality.

Keywords: hospitality management, foreign guests, Japanese lodging industry, Omotenashi

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Authors: Yu-Chen Wei, Yang-Cheng Lu, I-Chi Lin

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This study examines the impacts of news sentiment and press freedom on abnormal returns during the earnings announcement in greater China including the Shanghai, Shenzhen and Taiwan stock markets. The news sentiment ratio is calculated by using the content analysis of semantic orientation. The empirical results show that news released prior to the event date may decrease the cumulative abnormal returns prior to the earnings announcement regardless of whether it is released in China or Taiwan. By contrast, companies with optimistic financial news may increase the cumulative abnormal returns during the announcement date. Furthermore, the difference in terms of press freedom is considered in greater China to compare the impact of press freedom on abnormal returns. The findings show that, the freer the press is, the more negatively significant will be the impact of news on the abnormal returns, which means that the press freedom may decrease the ability of the news to impact the abnormal returns. The intuition is that investors may receive alternative news related to each company in the market with greater press freedom, which proves the efficiency of the market and reduces the possible excess returns.

Keywords: news, press freedom, Greater China, earnings announcement, abnormal returns

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