Sentiment Analysis for the Mizo Language: A Comparative Study of Classical Machine Learning and Transfer Learning Approaches (2023)

Abstract: Sentiment analysis, a subfield of natural language processing (NLP) has witnessed significant advancements in the analysis of usergenerated contents across diverse languages. However, its application to low-resource languages remains a challenge. This research addresses this gap by conducting a comprehensive sentiment analysis experiment in the context of the Mizo language, a low-resource language predominantly spoken in the Indian state of Mizoram and neighboring regions. Our study encompasses the evaluation of various machine learning models including Support Vector Machine (SVM), Decision Tree, Random Forest, K-Nearest Neighbor (K-NN), Logistic Regression and transfer learning using XLM-RoBERTa. The findings reveal the suitability of SVM as a robust performer in Mizo sentiment analysis demonstrating the highest F1 Score and Accuracy among the models tested. XLM-RoBERTa, a transfer learning model exhibits competitive performance highlighting the potential of leveraging pre-trained multilingual models in low-resource language sentiment analysis tasks. This research advances our understanding of sentiment analysis in lowresource languages and serves as a stepping stone for future investigations in this domain.

## introduction the ever-growing digital landscape has given rise to an abundance of user-generated content across various online platforms. this proliferation of text data, often accompanied by valuable insights and opinions, has sparked significant interest in the field of sentiment analysis. sentiment analysis, a subset of natural language processing (nlp), seeks to discern the emotional tone and opinions expressed within textual content @xcite . its applications span from understanding customer sentiment for business intelligence @xcite to monitoring public sentiment in the political sphere @xcite . while sentiment analysis has seen considerable progress in many widely spoken languages, it has encountered substantial challenges when applied to low-resource languages such as the mizo language. the mizo language, predominantly spoken in the indian state of mizoram and neighboring regions belongs to the kuki-chin branch of the sino-tibetan language family. it holds cultural and regional significance yet remains relatively underrepresented in the domain of computational linguistics. the mizo language serves as the gateway to understanding the sentiments and opinions of the mizo-speaking community in their native tongue and unlocking this holds immense potential for diverse applications including social media monitoring, community feedback analysis and local sentiment-driven decision-making. in addressing the compelling need to develop sentiment analysis models tailored to low-resource languages like mizo, it is crucial to delve into some linguistic details. the mizo language being part of the kuki-chin branch shares linguistic affiliations with languages like hmar, lushai (mizo), and paite. the mizo language also diverges from english not only in structure and word order but in its declarative sentence construction which follows an object-subject-verb (osv) order. in its basic or unmarked word order, mizo tends to build sentences in a way where the main elements such as the subject or object come first on the left side and additional information is added to the right. instead of using prepositions (like "under" or "over"), mizo uses postpositions which are similar but come after the noun they modify. for example, instead of saying "on the table", in mizo, we say "table chungah" which literally translates to "the table on". the positioning of auxiliary verbs after the main verb is a distinctive feature as it contrasts with the typical positioning found in many other languages; consider the english sentence "she has completed the homework" which translates to "homework a zo tawh". here, "tawh" is an auxiliary verb indicating completion or emphasis. adverbs are also mainly placed at the end of the sentence after the verb, eg : "i am not going" will be "ka kal dawn lo" where "lo" signifies the negation. mizo has a sophisticated system of agreement in which various parts of a sentence (like subjects and objects) can have matching features. this complexity allows for the omission or dropping of pronouns in certain cases, known as 'heavy pro drop.' for eg: "he sings a song and everyone loves him" can be written as "hla a sa a, an duh hle"; here the pronoun for him is not included in the mizo version due to mizo's rich agreement patterns and the ability to mark features on verbs that align with subjects and objects. ## literature survey sentiment analysis in natural language processing (nlp) has emerged as a dynamic field, driven by the need to comprehend the emotional tone and attitudes expressed in the ever-expanding realm of user-generated content across diverse platforms @xcite . the applications of sentiment analysis span various domains including business brand surveys, social media studies, and movie analyses @xcite . this process involves categorizing opinions into positive, negative, or unbiased classes offering valuable insights into product popularity and market dynamics @xcite . in response to the growing demand for sentiment analysis tools, researchers have developed resources like pysentimiento, a library facilitating sentiment analysis in multiple languages, such as spanish and english @xcite . the advancement of deep learning models including long-short term memory and attention models has significantly improved the accuracy of sentiment analysis tasks @xcite @xcite . additionally, lexiconbased approaches and traditional machine learning techniques like naive bayes and support vector machines (svm) continue to be widely employed in sentiment analysis @xcite @xcite . while sentiment analysis has made substantial progress in well-represented languages, research in low-resource languages has been comparatively limited due to language complexity and resource constraints. however, recent studies have addressed this gap, contributing to the exploration of sentiment analysis in linguistically diverse and underrepresented languages. for instance, in the case of manipuri language, the authors @xcite perform languagespecific pre-processing tasks such as transliteration building negative morpheme-based lexicon and filtering noisy words which improve classification results. some of the other low resource language works on sentiment classification of the social media contents are also found in @xcite @xcite . in the context of codemixed languages, (roy, 2023) proposed an ensemble model for predicting sentiment in code-mixed kannada and malayalam achieving promising results. in the realm of low-resource african languages, @xcite highlighted the significance of adapting pretrained models to target languages and tasks demonstrating significant performance improvements. @xcite explored classical machine learning and neural network-based techniques for sentiment analysis of central kurdish emphasizing the effectiveness of data augmentation. moreover, research on sentiment analysis in roman urdu by @xcite involved the creation of a dedicated corpus and the application of various models with the cnn-lstm method yielding the most favorable results. the study by @xcite proposed dlan, an end-to-end sentiment analysis architecture utilizing adversarial learning and knowledge distillation for crosslingual sentiment analysis showcasing improved performance compared to baseline methods. @xcite proposed a model that uses lexical features like adjectives, adverbs, and verbs to classify the sentiment polarity of assamese news article and their model showed improvement over the baseline model in terms of f1-score on a standard dataset. while acknowledging these contributions, this paper aims to extend the discourse by addressing sentiment analysis in the mizo language. ## methodology in this section, we outline the methodology employed for collecting, preprocessing, and annotating the datasets used in our sentiment analysis research with a focus on data collection, compilation, and the labeling process. ## conclusion and future directions this study contributes to the understanding of sentiment analysis in a low-resource language called "mizo" and sheds light on the suitability of various machine learning models and approaches. svm emerges as a strong performer, while transfer learning with xlmr-roberta offers a promising avenue for enhancing sentiment analysis capabilities in resource-scarce language contexts. future studies should prioritize the development of more comprehensive and balanced datasets employing effective data augmentation and sampling techniques to rectify imbalances and improve the models' ability to generalize across various sentiment categories. implementing advanced model training techniques including hyperparameter tuning and regularization strategies will be also pivotal in optimizing the models' learning processes and improving their predictive accuracy across a diverse range of sentiment inputs. by heeding these recommendations, future research endeavors can build upon the insights gained from this study and contribute to the development of more effective and reliable sentiment analysis methodologies for the mizo language.