Language Preference for Expression of Sentiment for N epali- E nglish Bilingual Speakers on Social Media (2023)

Abstract: Nepali-English code-switching (CS) has been a growing phenomenon in Nepalese society, especially in social media. The code-switching text can be leveraged to understand the socio-linguistic behaviours of the multilingual speakers. Existing studies have attempted to identify the language preference of the multilingual speakers for expressing different emotions using text in different language pairs. In this work, we aim to study the language preference of multilingual Nepali-English CS speakers while expressing sentiment in social media. We create a novel dataset for sentiment analysis using the public Nepali-English code-switched comments in YouTube. After performing the statistical study on the dataset, we find that the proportion of use of Nepali language is higher in negative comments when compared with positive comments, hence concluding the preference for using native language while expressing negative sentiment. Machine learning and transformer-based models are used as the baseline models for the dataset for sentiment classification. The dataset is released publicly.

## introduction in recent years, use of social media and computer mediated communication has increased with millions of users everyday. this increase in social media has consequently increased the use of code switching (cs) or code mixing content. cs can be broadly defined as the linguistic behavior of comprehending the language that is composed of lexical items and grammatical structure from two or more languages with no change of the interlocutor or topic. throughout this paper, we adopt the stance that the terms 'code switching' and 'code mixing' are used interchangeably to refer to the phenomenon of alternating between two or more languages within a single discourse. although there may be subtle nuances in usage within certain linguistic contexts, for the purpose of our study, both terms are treated as synonymous and describe the same linguistic behavior. cs was earlier associated with the spoken language, but due to the informal nature of social media, cs is also found in written form @xcite . the language spoken by multilingual individual is closely connected to emotion @xcite . similarly, emotion is a driving factor for cs behaviour @xcite . linguistics researchers have found that multilingual speakers have a certain language of preference for expressing their emotions @xcite . hence, the task of sentiment analysis and socio-linguistic studies based on the sentiment of multilingual speakers have received a lot of attention in the nlp domain. these studies have shed light on different characteristics of the society. several studies have analyzed the language preference in multilingual societies and concluded that multilingual speakers indeed prefer their first language (l1) while conveying their emotions @xcite . on the other hand, most studies in the field of codeswitching have only focused on the high-resource language pairs. up to now, far too little attention has been paid to leveraging the growing amount of nepali-english cs text in social media and analyzing the language preference for the sentiment emotions for nepalese multilingual community. sentiment analysis is a computational technique used to determine the sentiment or emotional attitude conveyed in a text. sentiment analysis can help in obtaining insights from the opinion on certain products or subjects of interest from the users and help in planning the business strategies @xcite . the applications and resources for sentiment analysis are mostly created for high-resourced languages in monolingual settings. however, the annotated data for monolingual data cannot handle code-switched scenarios and fails to leverage good results @xcite . several researchers have constructed the sentiment analysis dataset for codeswitched scenarios @xcite . however, to the best of our knowledge, there is no existing sentiment analysis dataset for code-switched nepali-english language even though nepali-english mixed language has emerged as a dialect in the nepalese community owing to the increasing use of english elements in nepali conversation. @xcite . in this study, we collect the public comments in code-switched nepali-english from youtube platform and annotate them with sentiment annotations. we hypothesize two different hypotheses to analyze the relation between the language used in the comment with the sentiment of the comment and preferred language for expression of negative or positive sentiments. the contributions of this study are as follows: ## related work the preferred language for expression of opinions by multilinguals has been studied by linguists for a long time. @xcite , studies the behavior of english-spanish bilinguals and report the use of english for professional purposes and spanish for informal purposes like chatting. @xcite studies the pragmatic functions of basque-spanish code-switching and made several conclusions, one of them being: basque-spanish multilingual speakers normally switch to spanish to convey humor and irony. @xcite identifies how multilingual speakers highly use their first language for swearing and taboo words. the authors report that the multilingual speakers, while using code-switching/mixing, tend to use their first language for swearing even when the language is not understood by their interlocutor(s). hindi and nepali languages are closely related with each other and belong to the same language fam-ily. for hindi-english code-switched data, @xcite analyze the english-hindi codeswitching and swearing pattern on social networks and conclude that the multilingual speakers have strong preference for swearing in the dominant language. @xcite study different aspects of english-hindi code-switching in twitter and identify the preference of expressing negative sentiments using hindi language is twice as much as english. in the context of nepali-english codeswitching, the study by @xcite presents a detailed socio-linguistic study on cs phenomenon in the conversations between nepalese people. this study studies the extent, role of media, and reason in mixing of nepali-english languages. to the best of our knowledge, there is no existing study that is focused on studying the language preference in nepali-english code-switched scenarios. computational linguists have been studying code-switching for a substantial period of time. several data resources have been created for the support of research on code-switching. @xcite release code-switched dataset for language identification tasks in four language pairs, nepali-english being one of them. they extract the sentences from social media platforms like twitter and facebook. similarly, @xcite release the sentiment dataset for code-switched hindi-english and spanish-english language pairs. the datasets constitutes of code-switched tweets with sentiment annotation among three classes: positive, neutral, and negative. a considerable amount of literature has been published utilizing youtube comments as a source of sentiment (or opinion) text for low-resource language mixed with english @xcite @xcite . while nepali-english code-switching has been a growing phenomenon in nepalese society, especially in social media, there is no sentiment analysis dataset focusing on code-mixed scenarios. hence, for the study of the language preference for expressing sentiment in code-switched nepali-english, we create a sentiment analysis dataset and perform the tests on our hypotheses. tigate this phenomenon using the proportions of words from certain languages used to express certain sentiments. we define two hypotheses to test in this study: hypothesis i: there is an association between sentiment and language proportions. ## baseline classifiers traditional machine learning models and transformer-based models are applied for determining the sentiments from the youtube comments as the simple baseline. the models used in this study are listed in this section.