Habesha@ D ravidian L ang T ech: Utilizing Deep and Transfer Learning Approaches for Sentiment Analysis. (2023)

Abstract: This research paper focuses on sentiment analysis of Tamil and Tulu texts using a BERT model and an RNN model. The BERT model, which was pretrained, achieved satisfactory performance for the Tulu language, with a Macro F1 score of 0.352. On the other hand, the RNN model showed good performance for Tamil language sentiment analysis, obtaining a Macro F1 score of 0.208. As future work, the researchers aim to fine-tune the models to further improve their results after the training process.

## introduction the recent rise of the internet as websites, blogs, social networks, online portals, and content-sharing services contribute to the huge amount of usergenerated sentiment texts @xcite . these sentiment texts are very important to get feedback from people. thus, the sentiment analysis fields that help to automatically examine sentiment from texts are required in various areas. sentiment analysis is a task of subjectivity analysis with some linkage to effective computing principles @xcite . sentiment analysis is an interdisciplinary theme that exploits natural language processing (nlp) methods, text mining, and computational linguistics to recognize and extract subjective information from source materials @xcite . it involves classifying the sentiment data into different polarity levels such as positive, negative, and neutral. sentiment analysis is used by the sectors such as advertisers, movie creators, booksellers, political parties, supermarkets, industries, restaurants, etc. that demand their customers' feedback on a particular issue to improve themselves afterward. for example, (1), sentiment analysis is used in government elections to examine the people's sentiments, appraisals, attitudes, and emotions on the candidates of the election. (2), business companies introduce new products to the market and then usefully extract the sentiment of the people on the internet about the product, and settle the future feasibility of this new product. it is possible to gather the sentiment from customers using surveys, blogs, and suggestion committees but this may result in a waste of resources and not provide us the comment in a short period. automatically gathering and classifying the sentiment that is expressed by natural language on social networks eradicates those problems with minimum time and resources. the objective of this work is to develop a sentiment analysis for tamil and tulu languages adopting the pre-trained model and deep learning algorithm and to evaluate the model's performance. getting the sentiment of people in the business sector organizations engaged leads to success in the field or business @xcite . the rest of the study is organized as follows. currently, opinions of the people towards any organization in the world are collected by holding different conferences/meetings by using oral and manual collection methods. such ways of gathering people's feedback consume much time and resources. in order to solve this, knowing what people are interacting with in their day-to-day life helps us to collect sentiments regarding some issues in a little time. nowadays, the internet technology that is invented in the world like facebook, twitter, blogs, websites, etc. is usable in using tulu and tamil language. so, the tamil and tulu language usage on internet technology provides us with massive sentiment data. even though the tamil and tulu language sentiment data is generated by different organizers, there is no sufficient sentiment analysis task that classifies the sentiment text into their polarity level. thus, collecting this sentiment and classifying it into their polarity level is the intention of this work. this may optimize oral and survey-based sentiment collection by adopting pre-trained and deep-learning algorithms. thus, the aim of this research is: in this part, related sentiment analysis research investigating tamil or tulu languages using different approaches is presented. besides, the recent sentiment analysis task which is developed by state of art methods is reviewed. (thavareesan and mahesan, 2018) performed a review to analyze the recent literature in the area of sentiment analysis in tamil texts. this study considered the preprocessing, corpus and techniques, and success rate for review. the paper finds that the performance of the model relies on the preprocessing steps such as negation handling and stop word removal. the review concluded that svm and rnn classifiers taking word level feature representation using tf-idf and word2vec provide better performance than grammar rules-based classifications and other classifiers with usage of words, tf, and bow features. the review revealed that resources such as tamil sentiwordnet, adjective rules, and n-grams also can be used in sa on tamil text as it proves a significant performance. to analyze the sentiment content of tamil movie reviews sentiment analysis task is investigated by @xcite . the aim of this work was to categorize the sentiment of tamil movies based on tamil tweets using tamil sentiwordnet (tswn). term frequency -inverse document frequency (tf-idf) feature extraction methods are applied to identify the sentiment polarity of the tamil movie data set. in this work, tamil sentiwordnet is used with adjectives to classify the sentiment. since the adjectives are not sufficient to detect the sentiment in the tamil texts as future work, the paper intended to use adverbs in the tamil language to identify the sentiment. finally, the paper recommended the adjective wordnet for any language is appropriate for the sentiment analysis. this research (roy and kumar, 2021) developed the sentiment analysis on tamil code-mixed text using bi-lstm. the code-mixed data like hindi-english, malayalam-english, and similar ones; are used to detect the sentiment from tamil review data. the proposed bi-lstm framework automatically extracts the features from input sentences and predicts their sentiment with a 0.552 f1 score for the best case using. @xcite studied emotion analysis of tamil texts using language agnostic embeddings. the paper invented a multilingual transformer model for the emotion analysis of tamil text as required by the dravidiantech-lang acl 2022 shared task. in this paper, labse, a pre-trained language agnostic bert model, was found to perform comparatively well on the tamil dataset. the paper claimed the result of tamil sentiment analysis can be optimized by utilizing custom embeddings, based on a statistical analysis of the language, to process the data before training the model. a code-diverse tulu-english data for nlp-based sentiment analysis applications are developed by this paper @xcite . the research concentrated on nlp for emotion and sentiment detection of tulu, a vibrant south indian language, to start with. development of the standard corpus for nlp applications of code-diverse text in tulu-english is contributed by this work. the performance analysis of the dataset is performed through krippendorff's alpha value of 0.9 indicates that it is a benchmark in the development of the automatic sentiment analysis system for tulu. though research @xcite , @xcite are conducted for different languages using deep learning and transformer learning the tulu and tamil sentiment analysis is very limited using these methods. ## task description to identify the sentiment polarity of the codemixed dataset of comments and posts in tamil-english and tulu-english collected from social media by @xcite is used. the comment/post may contain more than one sentence but the average sentence length of the corpora is 1. each comment or post is annotated with sentiment polarity at the comment or post level. this dataset also has class imbalance problems depicting real-world scenarios. our proposal aims to encourage research that will reveal how sentiment is expressed in code-mixed scenarios on social media. the purpose of this task is to perform sentiment analysis for tulu and tamil languages. the given tulu sentiment text is classified into mixed feelings, negative, neutral, and positive. on the other hand, the tamil sentiment data is classified into classes such as negative, mixed feelings, positive and unknown @xcite . the given data is full of noise and to upgrade the performance of the model the data cleaning task is performed at the pre-processing stage for both languages. the participants were provided with the tamil and tulu development, training, and test dataset. this is a message-level polarity classification task. given a youtube comment, systems have to classify it into positive, negative, neutral, or mixed emotions. the participants will be provided development, training, and test dataset code-mixed text in dravidian languages (tamil-english and tulu-english). the proposed framework incorporates data preprocessing, feature extraction, training a model, and sentiment classification evaluation. the dataset used in this study is found on dravidian-langtech2023. majorly the dataset of the tamil language belongs to the positive sentiment class. the rest of the tamil data set is distributed to negative, mixed feelings, and unknown state sentiment classes. likewise, the tulu sentiment is majorly included as a positive sentiment class and the others are distributed as negative, neutral, and mixed feeling sentiment categories. figure 2 shows the distribution of the tamil and tulu sentiment analysis data. based on the provided figure, it is evident that there is a limited quantity of both development and training data. the only exception is the training data for the tamil language, although even these datasets are unbalanced. as a result, this situation causes the model to exhibit reduced or inaccurate performance. ## conclusion in this research paper, we have trained a bert and rnn model for the sentiment analysis of tamil and tulu texts as the need of dravidianlangtech 2023-ranlp 2023. a pre-trained bert language bert model performed well for the tulu language comparing it with the tulu language yielding a macro f1 score of 0.352. in another way, the rnn model performance is good for tamil language sentiment analysis with a macro f1 score of 0.208. the researchers take as future work fine-tuning the models to improve the results of the models after training.