W ord W izards@ D ravidian L ang T ech 2024: Sentiment Analysis in T amil and T ulu using Sentence Embedding (2024)

Abstract: Sentiment Analysis of Dravidian Languages has begun to garner attention recently as there is more need to analyze emotional responses and subjective opinions present in social media text. As this data is code-mixed and there are not many solutions to code-mixed text out there, we present to you a stellar solution to DravidianLangTech 2024: Sentiment Analysis in Tamil and Tulu task. To understand the sentiment of social media text, we used pre-trained transformer models and feature extraction vectorizers to classify the data with results that placed us 11th in the rankings for the Tamil task and 8th for the Tulu task with a accuracy F1 score of 0.12 and 0.30 which shows the efficiency of our approach.

## introduction social media transcends borders, fostering global communication with user-generated content expressing emotions. research, like wang et al.'s study @xcite , uses ensemble models to detect depression signs from social media. the surge in platforms underscores the importance of sentiment analysis in local languages, as seen in the study on dravidian languages like tamil @xcite . english intertwining with native scripts poses challenges, and natural language processing techniques are suggested for effective sentiment analysis, focusing on specific subjects. the task involves classifying youtube comments into labels for tulu and tamil, aligning with findings that sentiment analysis in code-mixed text requires careful consideration of linguistic nuances in multilingual contexts. the rest of the paper is organized as follows. section 2 outlines the related works emphasizing sentiment analysis in dravidian languages. section 3 presents a description of the dataset. section 4 describes the methodology used for the shared task. section 5 discusses the result and analysis of the task assigned. in section 6 concludes the paper followed by ethics statement in section 7. ## related works sentiment analysis is vital amid the vast data on social media. approaches include lexicon-based methods, using dictionaries like sentiwordnet and tf-idf (term frequency-inverse document frequency), and machine learning methods employing svm and naïve bayes models. combining these methods enhances efficiency, addressing unstructured data effectively @xcite . research on sentiment analysis of dravidian languages, exemplified by chakravarthi et al.'s study @xcite , explores challenges in linguistic diversity and code-mixing, emphasizing the need for tools handling codemixed text (s. @xcite . the findings underscore the importance of linguistic considerations in capturing sentiments in dravidian languages and english on social media @xcite , providing valuable insights into public opinion, cultural discourse, and community dynamics. this highlights the necessity of language-specific sentiment analysis tools for understanding sentiments in diverse linguistic communities. ## dataset the datasets provided were comments from social media and each comment corresponded to a particular label. the labels were positive for comments that were appreciative, negative for hate speech and other detected signs on aggression, mixed feeling/feelings for comments on the fence and neutral/unknown_state for comments that were neither positive nor negative. the dataset was divided into three parts namely train,test and dev. the train and dev datasets had text and category as labels while the test dataset had id and text as labels for tamil @xcite . the train and dev datasets had text and annotations as labels while the test dataset had id and text as labels @xcite for tulu. the test data for both languages was not labeled and we had to find and classify the test data as part of the task. the train dataset consisted of 33,988 rows of which positive: 20,070 unknown state: 5,628 negative: 4,271 mixed feelings: 4,020 for tamil and 6945 rows of which positive: 3,352 neutral: 1,854 mixed feeling: 1,041 negative: 698 for tulu. the dev dataset consisted of 3,785 rows for tamil and 500 rows for tulu. the test dataset consisted of 650 rows for tamil and 502 rows for tulu. 4 methodology 4.1 preprocessing preprocessing refers to the ways in which we can clean data and remove noise (inconsistencies). these methods are applied to raw data so we may process them better later on. preprocessing involves many steps which include: ## conclusion this study explores sentiment analysis in dravidian languages, tamil and tulu, using labse for feature extraction and knn for classification. achieving competitive rankings in the dravidianlangtech 2024 competition (11th for tamil, 8th for tulu), our model demonstrated promising precision and recall, particularly excelling in handling uncertain sentiments in the "unknown state" class for tamil. while acknowledging limitations and scalability challenges, we emphasize ethical considerations, including privacy and cultural sensitivity. in conclusion, our research provides insights into effective strategies for sentiment analysis in code-mixed social media text in dravidian languages, showcasing the viability of labse and knn in culturally diverse contexts.