JCT at S em E val-2020 Task 12: Offensive Language Detection in Tweets Using Preprocessing Methods, Character and Word N-grams (2020)

Abstract: In this paper, we describe our submissions to SemEval-2020 contest. We tackled subtask 12 - “Multilingual Offensive Language Identification in Social Media”. We developed different models for four languages: Arabic, Danish, Greek, and Turkish. We applied three supervised machine learning methods using various combinations of character and word n-gram features. In addition, we applied various combinations of basic preprocessing methods. Our best submission was a model we built for offensive language identification in Danish using Random Forest. This model was ranked at the 6 position out of 39 submissions. Our result is lower by only 0.0025 than the result of the team that won the 4 place using entirely non-neural methods. Our experiments indicate that char ngram features are more helpful than word ngram features. This phenomenon probably occurs because tweets are more characterized by characters than by words, tweets are short, and contain various special sequences of characters, e.g., hashtags, shortcuts, slang words, and typos.

## introduction nowadays, social networks occupy an increasingly important place in our life. however, their rapid development has also allowed the development of increasingly offensive, hateful content that takes advantage of the anonymous nature of the cyber world. this bullying, trolling, and harassment content has become a growing concern for governments, organizations, and individuals. the use of machine learning (ml) and natural language processing (nlp) solutions to find this offensive content has been surprisingly useful. still, the detection of offensive language from social media is not an easy research problem due to the different levels of ambiguities present in natural language and the noisy nature of social media language. in addition, social media subscribers come from linguistically diverse communities. up to now, most research has focused on the detection of offensive language in english but works have also been written for other languages like @xcite for @xcite for spanish. task 12-a of semeval 2020 deals with the detection of offensive language in tweets in five languages including english. in this paper, we describe our models submitted to subtask 12-a for tweets written in four languages arabic, danish, greek, and turkish. the full description of task 12 is given in @xcite . the structure of the rest of the paper is as follows. section 2 introduces a background concerning offensive language, tweet classification, and data preprocessing. section 3 describes subtask 12 and its datasets. in section 4, we present the submitted models and their experimental results. section 5 summarizes and suggests ideas for future research. ## task description semeval-2020 task 12 @xcite addresses the problem of detecting offensive language in tweets.it is a based on the last edition: semeval-2019 task 6. @xcite . for arabic @xcite , danish (sigurbergsson and derczynski, 2020), english @xcite , greek @xcite , and turkish (c ¸öltekin, 2020), task 12 deals with the identification of offensive language in tweets. that is to say, we have to classify each tweet as offensive (off) or not offensive (not). for english, there were two additional tasks: automatic categorization of offense types and offense target identification. as mentioned and explained above, we did not participate in tasks for the english language. a development dataset has been provided for arabic but not for the other languages in which we split the provided training dataset and allocate 20% of this data for development. ## the submitted models and experimental results the two co-authors of this paper are from different academic places: bar-ilan university (biu) and jerusalem college of technology (jct). different models have been applied and submitted from two users biu and jct. in this paper, we will present the best submitted jct models. we applied the following three supervised ml methods on the training datasets: random forest (rf), support vector classifier (svc), and multi-layer perceptron (mlp). ## conclusions and future research in this paper, we describe our submissions to subtask 12 of semeval-2020 contest. like said before, we developed several models for each language for four languages: arabic, danish, greek, and turkish, after what, we select the two best models while trying to choose two models that use a different method of supervised learning and submit them. our best submission was a model we built for offensive language identification in danish using random forest. this model was ranked at the 6th position out of 39 submissions. its macro f1-score result (0.7741) is lower by only 0.003 than the result of the team that won the 4th place. it is interesting to note that this is the only model among those in the top which does not use a neural method which show that classical methods still remains relevant. many tweets contain acronyms that can be presented in different forms. these acronyms can lead to ambiguity. future research may seek to lessen this ambiguity. acronym disambiguation, e.g. hacohen-kerner et al. (2010a), will extend and enrich the tweet's text and might enable better classification. we also suggest examining the usefulness of skip character n-grams because they serve as generalized ngrams that allow us to overcome problems such as noise and sparse data @xcite . other ideas that may lead to better classification are to use stylistic feature sets @xcite , key phrases @xcite , and summaries @xcite . we may also apply advanced deep learning methods and especially rnn, which is more suitable for text applications.