Selam@ D ravidian L ang T ech:Sentiment Analysis of Code-Mixed D ravidian Texts using SVM Classification (2023)

Abstract: Sentiment analysis in code-mixed text written in Dravidian languages. Specifically, Tamil- English and Tulu-English. This paper describes the system paper of the RANLP-2023 shared task. The goal of this shared task is to develop systems that accurately classify the sentiment polarity of code-mixed comments and posts. be provided with development, training, and test data sets containing code-mixed text in Tamil- English and Tulu-English. The task involves message-level polarity classification, to classify YouTube comments into positive, negative, neutral, or mixed emotions. This Code- Mix was compiled by RANLP-2023 organizers from posts on social media. We use classification techniques SVM and achieve an F1 score of 0.147 for Tamil-English and 0.518 for Tulu- English.

## introduction social media platforms have become significant sources of user-generated content, providing a wealth of information about people's opinions, emotions, and attitudes. sentiment analysis, a subfield of natural language processing, aims to automatically classify the sentiment or emotional tone expressed in textual data. currently, in the area of nlp, different researchers are developing different nlp applications in code-mixed datasets. some of the applications are code-mixed sentiments analysis which involves identifying subjective opinions or emotional responses, has gained significant attention in both academia and industry over the past two decades. one emerging challenge in sentiment analysis is the detection of sentiment in social media texts, particularly in dravidian languages, where code-mixing is prevalent @xcite . social media platforms have become more integrated into this digital era and have impacted various people's perceptions of networking and socializing (tonja et al., 2022) machine translation detection, sentiment analysis and language identification @xcite . code-mixing refers to the phenomenon where multiple languages or language varieties are used within a single conversation or text. this linguistic practice is prevalent in multilingual societies, particularly in regions where diverse languages coexist. in the context of dravidian languages, such as tamil and english, code-mixing has gained prominence on social media platforms, where users freely express their thoughts using a mixture of both languages. code-mixing or code-switching is the alternation between two or more languages at the level of the document, paragraph, comments, sentence, phrase, word, or morpheme. it is a distinctive aspect of conversation or dialogue in bilingual and multilingual societies @xcite as the classification model for sentiment analysis, we propose using the support vector machine (svm) algorithm. svm has proven effective in various natural language processing tasks, including sentiment analysis @xcite . by utilizing svm, we aim to leverage its ability to capture complex patterns in text data and its flexibility in handling high-dimensional feature spaces. in social media, low-resourced languages such as tamil and malayalam have been increasingly used along with english @xcite the contribution of this research lies in advancing the understanding of sentiment expression in code-mixed scenarios on social media, within the context of dravidian languages of tamil-english and tulu-english language. accurate identification of sentiment polarity at the message level provides valuable insights into user emotions and attitudes in code-mixed interactions. also known as opinion mining, @xcite is a natural language processing technique that aims to determine the sentiment expressed in a piece of text @xcite . by applying sentiment analysis to code-mixed interactions, researchers and analysts can gain a deeper understanding of how users feel and their attitudes toward specific topics or situations. accurate identification of sentiment polarity in codemixed interactions can provide valuable insights into user emotions and attitudes @xcite . advancements in code-mixed sentiment analysis can contribute to a better understanding of user sentiment in multilingual communities, social media, customer support, and other domains where codemixing is prevalent @xcite . the findings of this research will facilitate the development of more robust sentiment analysis techniques for analyzing multilingual social media data, enabling improved understanding and interpretation of user sentiments across diverse linguistic contexts @xcite . ## related work along with language-specific preprocessing techniques, the implemented model used sub-word level representations to incorporate features at the morpheme level, the smallest meaningful unit of any language. it was evaluated by weighted average f1-score, the subword level approach achieved the 5 th highest score 47 in the tamil task, and the 12 th rank in the malayalam task @xcite . people use code-mixing because it is much easier and more effective to express their feelings, grab the attention of others, and are not fluent in one of the languages used @xcite . as a multilingual country, people commonly mix or switch from their regional or native language to indonesian @xcite . it is frequently heard in daily conversations in the neighborhood or on social media. to improve abusive language detection in english social media communications, @xcite used the 'deepmoji' technique, which was first announced in 2017 @xcite . this strategy is primarily based on pretraining a neural network model for offensive language classification using emojis as poorly supervised training labels. a lexical syntactic feature architecture was proposed to strike a balance between identifying offensive content and potentially offensive users in social media @xcite the challenge of cross-lingual classification due to linguistic differences between languages. mentions that the svm and knn algorithms were effective for this task, showcasing the importance of selecting appropriate algorithms for different languages @xcite this data on polarity can help in understanding public opinion. furthermore, including sentiment analysis can improve the performance of tasks such as recommendation system @xcite to train some machine learning classifiers with various syntax-based n-gram features. ## task a description and dataset the shared task on sentiment analysis in tamil and tulu focuses on message-level polarity classification. the dataset provided for this task consists of code-mixed text in dravidian languages, namely tamil-english and tulu-english. tamil-english code-switched, sentiment-annotated corpus containing 15,744 comment posts from youtube. @xcite . and the codemixed tulu annotated corpus of 7,171 youtube comments is created. @xcite the comments and posts in the dataset may contain more than one sentence, but the average sentence length across the corpora is one. each comment and post is annotated with sentiment polarity at the message level, indicating whether it expresses a positive, negative, neutral, or mixed emotional sentiment. these datasets reflect the real-world scenarios of social media texts, exhibiting class imbalance issues commonly encountered in sentiment analysis tasks. the datasets provided in this task will facilitate the exploration of innovative approaches and techniques for sentiment analysis in multilingual and multicultural contexts. ## methods for sentiment analysis tasks in code-mixed comments and posts in tamil-english and tulu-english, we propose employing the support vector machines (svm) model as the classification technique. svm has proven effective in various natural language processing tasks, including sentiment analysis, and has demonstrated robustness in handling high-dimensional feature spaces. ## results in the shared task, a support vector machine (svm) model was employed for message-level polarity classification of code-mixed text in tamil-english and tulu-english. the evaluation metric used was the f1 score, which provides a measure of the model's performance across all sentiment classes. the results obtained for the svm model on the provided datasets were as follows in run language f1-score run1 tamil 0.147 run1 tulu 0.518 ## conclusion this study shows how different languages may be identified in code-mix data using a classifier that uses two algorithms, svm and tf-idf. the first technique produces better results, with the best weighted average f1-score of 0.147 and 0.518.