I nnovation E ngineers@ D ravidian L ang T ech- EACL 2024: Sentimental Analysis of Y ou T ube Comments in T amil by using Machine Learning (2024)

Abstract: There is opportunity for machine learning and natural language processing research because of the growing volume of textual data. Although there has been little research done on trend extraction from YouTube comments, sentiment analysis is an intriguing issue because of the poor consistency and quality of the material found there. The purpose of this work is to use machine learning techniques and algorithms to do sentiment analysis on YouTube comments pertaining to popular themes. The findings demonstrate that sentiment analysis is capable of giving a clear picture of how actual events affect public opinion. This study aims to make it easier for academics to find high-quality sentiment analysis research publications. Data normalisation methods are used to clean an annotated corpus of 1500 citation sentences for the study. .For classification, a system utilising one machine learning algorithm—K-Nearest Neighbour (KNN), Na ̈ıve Bayes, SVC (Support Vector Machine), and RandomForest—is built. Metrics like the f1-score and correctness score are used to assess the correctness of the system.

## introduction in the dynamic realm of social media, the role of sentiment analysis is increasingly crucial in comprehending the subtleties of user expressions. traditionally designed for high-resource languages and individual utterances, sentiment analysis tools are encountering new challenges amid bilingual communities and code-mixed writing styles. this project addresses the growing significance of sentiment analysis, specifically focusing on code-mixed tamil-english expressions prevalent across various social media platforms. supervised learning approaches, traditionally reliant on annotated data, face limitations when applied to code-mixed languages. notably, features based on lexical attributes, such as word dictionaries and parts of speech tagging, exhibit suboptimal performance in this multilingual context. to overcome these challenges, our research focuses on sentiment analysis within code-mixed tamil-english contexts. central to our approach is the implementation of the decision tree algorithm, which offers a robust solution for accurately classifying sentiments within this unique linguistic fusion. this project not only demonstrates exceptional accuracy using detailed metrics like precision, recall, and f1-score but also introduces a substantial corpus for underresourced code-mixed tanglish. marked by high inter-annotator agreement, this dataset serves as a valuable resource for researchers delving into sentiment analysis and linguistic phenomena in codemixed environments. positioned at the intersection of sentiment analysis, machine learning, and codemixed language research, our project extends beyond precise sentiment classification. it serves as a foundational resource for future investigations into the dynamic landscape of multilingual social media expressions. ## literature survey certainly, here's a brief list of literature surveys by various authors focusing on sentiment analysis in tamil nadu: the research paper work in @xcite demonstrates a sentiment analysis technique for tamil texts utilising k-means clustering and k-nearest neighbour classifier. despite different settings, the technique achieved an accuracy of 89.87% for the uj moviereviews corpus utilising fasttext and class-wise clustering. the main focus of the paper present in @xcite is sentiment analysis, a natural language processing task that involves determining the sentiment or emotion expressed in a given piece of text. in this case, the analysis is applied to tweets in both english and tamil.it is discovered that the suggested system's f-measure accuracy value, which makes use of svm, is 0.741. the primary focus of the paper work in @xcite is on sentiment analysis, particularly suited to data with mixed tamil codes. the term "code-mixing" describes the typical practice in multilingual cultures of combining two or more languages into a single statement or speech. svm achieves 75.9% classification accuracy for tamil movie reviews, a noteworthy achievement in tamil language study. the primary focus of the paper repersent in @xcite is on sentiment analysis,particularly applicable to data with mixed tamil codes. codemixing, a prevalent practice in multilingual cultures, is the blending of two or more languages inside a single statement or speech.the outcome shows that, with an accuracy of 0.66 using preprocessed tamil code-mixed data, the hybrid deep learning model in particular, the cnn+bilstm model performs better than all the other models used. the paper in soumya and pramod (2020) a review of machine learning methods for sentiment analysis of data with mixed tamil codes this study examines the impact of pre-processing on tamil code-mixed data using transfer learning, hybrid deep learning, deep learning, and traditional machine learning models. the study concentrates on removing emojis, punctuation, symbols, numerals, and repeating characters from the data. the hybrid deep learning model cnn+bilstm performs better with pre-processed tamil code-mixed data, with an accuracy of 0.66. the study compares the performance of these models with the state-of-the-art methods, including indicbert, logistic regression, random forest, multinomial naive bayes, and linear support vector classification. in order to increase the accuracy of sentiment analysis on social media data, future research should focus on multimodal data sets and context-based algorithms. the primary focus of the paper work in @xcite is predicting sentiment in reviews related to tamil movies using machine learning algorithms.with accurancy for categorising tamil movie reviews, svm yields a 75.9% accuracy rate. the paper inchakravarthi et al. (2020b)the dravidian-codemix-fire 2020 track focused on sentiment analysis for code-mixed tamil and malayalam in youtube comments. researchers aimed to classify sentiments using a weighted-f1 score, addressing linguistic complexities. the primary focus of the paper work inchakravarthi et al. (2020a) sentiment in social media comments is crucial for decisionmaking. this study addresses challenges in sentiment analysis, especially in code-mixed text from low-resourced languages like tamil, presenting a benchmark corpus and sentiment analysis results. the paper in hegde et al. (2022)sentiment analysis (sa) uses code-mixed data from social media for decision-making. however, low-resource languages like tulu struggle with annotated data. a gold standard corpus of 7,171 tulu comments is created, and machine learning algorithms are used to evaluate the dataset, showing encouraging performance. ## problem and system description the objective of the sentiment analysis project is to automatically analyse and categorize the sentiment expressed in a given text. the sentiment is classified into categories such as "positive", "negative" or "unknown state." the project aims to leverage machine learning, specifically the knn algorithm, to accurately predict the sentiment of textual data. ## dataset description the training dataset comprises 33990 samples of code-mixed tamil-english language, spanning diverse topics, with sentiment labels including positive, negative, mixed feelings, and unknown state. the text data underwent tf-idf vectorization, resulting in the creation of the train tfdf matrix. the decision tree classifier demonstrated exceptional accuracy, achieving approximately 99.97% on the training data. the test dataset comprises 649 samples of code-mixed tamil-english language. each sample includes a text segment unseen during training, serving to assess the model's generalization to new data. the dataset includes predicted sentiment labels generated by the trained decision tree classifier, indicating the model's predictions for the sentiments expressed in the text segments. ## predictions on test data text segments: the test dataset consists of 649 text segments in code-mixed tamil-english language. prediction labels: predicted sentiment labels were generated using the trained decision tree classifier, categorizing each text segment into sentiments such as positive, negative, mixed feelings, or unknown state. model generalization: the predictions on the test data showcase the model's ability to generalize its learned patterns to previously unseen text, providing insights into its performance on realworld, diverse language expressions. evaluation: the predicted sentiment labels can be compared with the ground truth labels, if available, to assess the model's accuracy and performance on this new, independent dataset. ## conclusion the project analyzes sentiment using the random-forest tree technique, and it achieves remarkable accuracy in a variety of classes. important parameters including precision, recall, and f1-score are shown in its thorough classification report. the project has the potential to influence linguistic study and is a useful resource for code-mixed research with a carefully annotated dataset. nonetheless, it encounters obstacles like as overfitting and broad generalization to other settings. despite these, the project is a commendable addition to sentiment analysis in code-mixed languages because of its solid base and available resources.