# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 1 Introduction  
Classification of sentiment is essential to text analysis. It is concerned with the automatic extraction of subjective data from text sources. The data provide a clear picture of the entities of interest, such as people, products, aspects, or concepts. The process assigns labels with varying granularity depending on the task. For instance, labels can be positive-negative [1], positive-neutral-negative [2], positive-negative-mixed-other [3], or positive-negative-neutral-mixed-other. Earlier work concentrated primarily on extracting well-designed features [4; 5; 6]. Recent work with neural networks simplifies the feature engineering required to extract features from input text [7]. The main challenge posed by such deep neural networks is the requirement for training data (supervision). The availability of such supervised resources is a challenge for languages with limited resources. Cross-lingual sentiment analysis (CLSA) makes use of resources from high-resource languages to construct a sentiment analyzer for low-resource languages. For instance, the simplest configuration translates instances of data from the target language to the source and applies the classifier trained on the high-resource source language [8]. Using machine translation (MT) systems to translate the resources from the source language (annotated datasets or lexicons) into the target language is an alternative method [9; 10]. However, such an accurate translation system is not always available for language pairs with limited resources. Similarly, a prior attempt employed parallel data [11]. Recent research utilizing word embeddings and context-sensitive representations, such as GPT [12], ELMO [13], BERT [14], and RoBERTa [15], has improved overall classification performance. Pretraining on large corpora is used to acquire these representations. In a multilingual environment, multiple languages are trained collectively on a single model. Downstream tasks like Named Entity Recognition and Classification (NERC) [16] or Question Answering (QA) [17] refine the pre-trained language models (PLMs). The primary challenge with multilingual pre-trained language models is the effect of similar languages and representation in the learned space. For instance, the Multilingual BERT (MBERT) [14],which has been trained in 104 languages, does not proportionally represent each language in terms of training corpus. Fine-tuning PLMs for under-represented languages results in poor performance for the target language. In addition to under-representation, many languages are absent from these PLMs. All of these conditions are a result of the lack of data for languages with limited resources. Sentiment classifiers trained on pre-trained language modeling tasks have demonstrated cutting-edge performance [18; 19]. Even though these approaches have been investigated in a cross-lingual context, their applicability to low-resource languages, particularly languages within the same language family, remains to be investigated. Our analysis investigates the transfer of knowledge between languages of the same language family. We seek the optimal means of combining source-language and target-language data sources. This article describes all the techniques and experimental analyses for combining high-resource languages with low-resource languages. Throughout the past decade, cross-lingual sentiment classification has remained an active field of study. [PERSON] and [PERSON] [20] classify cross-lingual processing approaches as either model transfer or annotation adaptation. Model transfer utilizes language-independent features. One of the ways to learn these characteristics is through adversarial training [21; 22]. These cross-lingual representations are optimized for the final task, such as the recognition and classification of parts of speech or named entities. Methods for annotation projection utilize massive parallel corpora between the source and target languages. They exploit the semantic similarity between the parallel corpora.