NAYEL at S em E val-2020 Task 12: TF / IDF -Based Approach for Automatic Offensive Language Detection in A rabic Tweets (2020)

Abstract: In this paper, we present the system submitted to “SemEval-2020 Task 12”. The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach has been used to design our system. We implemented a linear classifier with Stochastic Gradient Descent (SGD) as optimization algorithm. Our model reported 84.20%, 81.82% f1-score on development set and test set respectively. The best performed system and the system in the last rank reported 90.17% and 44.51% f1-score on test set respectively.

## introduction the tremendous usage of social media platforms makes it important to apply different natural language processing (nlp) tasks on these platforms. different tasks, such as cyberbullying identification, hate speech detection, sarcasm detection and offensive language detection attracted nlp researchers to concentrate on automation of these tasks @xcite . one of these tasks which gained a research interests is automatic offensive language detection. offensive language is widespread in social media. computational offensive language detection is a solution to identify such hostility and has shown promising performance @xcite . arabic is a significant language having an immense number of speakers as it is the official language of 22 countries @xcite . it is recognized as the 4th most used language of the internet @xcite . the research in nlp for arabic is constantly increasing @xcite . automatic offensive language detection becomes an important nlp task due to the overwhelming usage of social media. automatic offensive language identification in arabic is a challenge due to the complexity of arabic language @xcite . in this paper we describe the model that has been submitted to the offensive language detection shared task "offenseval 2020" @xcite . given a tweet, then the task in brief is to determine whether it contains an offensive language or not. the first version, "offenseval 2019", was held at semeval 2019 @xcite . a dataset containing english tweets and annotated using a hierarchical three-level annotation model has been used in "offenseval 2019" @xcite . in "offenseval 2020", in addition to english, four more languages have been added to the dataset namely, arabic, danish, greek and turkish. we participated in "offenseval 2020" for arabic. a machine learning based approach has been used to develop our submission. term frequency/ inverse document frequency (tf/idf) vector space model has been used to represent the given tweets. neural network (cnn) and bidirectional long short-term-memory (bilstm) for offensive language detection. nayel and shashireka used classical machine learning algorithms to detect hate speech for multi-lingual tweets (nayel and l, 2019). ## task description given a tweet, the objective of the task is to determine if the tweet contains offensive language or not. suppose @xmath0, off}, a set of two classes where not is the class of non-offensive tweets and off is the class of offensive tweets. we have formulated the task as a binary classification problem that assigns one of the two predefined classes of c to a new unlabelled tweet. ## methodology our approach depends on tf/idf vector space model, convert the tweet into a vector and then apply the linear classifier on the vector space. linear classifier is a simple classifier that uses a set of linear discriminant functions to distinguish between different classes @xcite . ## conclusion in this working notes, a model which performs satisfactorily in the given task has been presented. the model is based on a simple framework, where tf/idf was used as as weighting scores and classical machine learning algorithms as classifiers. the improvement of our work can be done using deep learning architecture with better word representation. another hitch of the model is that it does not use any external data other than the provided dataset which may affects results based on the small size of the data. investment of the related domain knowledge may improve the performance of the model.