SSNCSE _ NLP @ D ravidian L ang T ech- EACL 2021: Offensive Language Identification on Multilingual Code Mixing Text (2021)

Abstract: Social networks made a huge impact in almost all fields in recent years. Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. Everyday we have to deal with texts, emails or different types of messages in which there are a variety of attacks and abusive phrases. It is the moderator’s decision which comments to remove from the platform because of violations and which ones to keep but an automatic software for detecting abusive languages would be useful in recent days. In this paper we describe an automatic offensive language identification from Dravidian languages with various machine learning algorithms. This is work is shared task in DravidanLangTech-EACL2021. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. This work explains the submissions made by SSNCSE_NLP in DravidanLangTech-EACL2021 Code-mix tasks for Offensive language detection. We achieve F1 scores of 0.95 for Malayalam, 0.7 for Kannada and 0.73 for task2-Tamil on the test-set.

## introduction social media has become an important tool to connect the people all over the world. this is because it allows its users to share the content they want quickly, efficiently and in real-time. however, usercreated content shared on social media is not always organized by the rules. in fact, nowadays, content written in an offensive language has become widespread on social media @xcite @xcite . offensive language is defined as message which contains insulting or threatening expressions written by one person to another @xcite . the severity of this problem is increasing each day; consequently, it is very important to deal with this problem in terms of government policy, social media terms and policies and online community plans @xcite . at this stage, there is a need for effective methods. social media generates a large amount of data daily as mentioned above. therefore, it is very difficult to manually determine offensive language on the social media even by an expert. some words that have multiple meanings that could be offensive to some people from some places. there is an increasing demand for offensive language identification on social media texts which are largely code-mixed (chakravarthi, 2020). code-mixing is a prevalent phenomenon in a multilingual community and the code-mixed texts are sometimes written in non-native scripts @xcite . systems trained on monolingual data fail on code-mixed data due to the complexity of code-switching at different linguistic levels in the text @xcite . this shared task on offensive language identification in dravidian languages-eacl 2021 presents a new gold standard corpus for offensive language identification of codemixed text in dravidian languages (tamil-english, malayalam-english, and kannada-english). a recorded tamil writing has been archived for more than 2600 years @xcite . the oldest time of tamil writing, sangam writing, is dated from ca. 600 bc -ad 300. it has the most established surviving writing among dravidian languages. over 55% of the epigraphical engravings (around 55,000) found by the archeological survey of india are in the tamil language. ## related work offensive language identification for greek language is described in @xcite . this paper uses different machine learning and deep learning models to for offensive language identification task with offensive greek tweet dataset. multilingual offensive language identification using cross lingual embeddings with transfer learning is carried out in @xcite @xcite . in @xcite , flame detection approach which extracts features at different conceptual levels and applies multilevel classification for flame detection was described. arabic offensive language identification task was described in @xcite . the survey of offensive language identification task is explained in @xcite . machine learning approach for detecting offensive language identification on twitter data is carried out in @xcite . ## data-set analysis and preprocessing the goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in dravidian languages ( (tamil-english, malayalam-english, and kannada-english)) collected from social media @xcite @xcite . the comment/post may contain more than one sentence but the average sentence length of the corpora is 1. each comment/post is annotated at the comment/post level. this dataset also has class imbalance problems depicting real-world scenarios. the dataset containing youtube comments with class labels such as not-offensive, offensive-untargeted, offensive-targeted-individual, offensive-targetedgroup, offensive-targeted-other, or not-in-indentedlanguage. the train-set, dev-set and test-set distribution with class-wise distribution is shown in ## experimental setup and features for feature extraction, the n-gram model and bert embedding model are experimented upon. as the content of the comments is a mix of dravidian language grammar in roman lexicons along with english grammar, it becomes challenging to find ## conclusion there is an increasing demand for offensive language identification on social media texts which are largely code-mixed. the goal of this task is to identify offensive language content of the codemixed dataset of comments/posts in dravidian languages ( (tamil-english, malayalam-english, and kannada-english)) collected from social media. in the proposed work, the basic tfidf and count vectorizer features are giving better performance when compared to sentence embeddings. the examples are not sufficient to train the deep learning models. the machine learning models are giving better performance than the deep learning models.