What is the Random Kitchen Sink approach?
Random Kitchen Sink method uses a kernel function to map data vectors to a space where linear separation is possible.
Offensive behaviour has become pervasive in the Internet community. Individuals take the advantage of anonymity in the cyber world and indulge in offensive communications which they may not consider in the real life. Governments, online communities, companies etc are investing into prevention of offensive behaviour content in social media. One of the most effective solution for tacking this enigmatic problem is the use of computational techniques to identify offensive content and take action. The current work focuses on detecting offensive language in English tweets. The dataset used for the experiment is obtained from SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The dataset contains 14,460 annotated English tweets. The present paper provides a comparative analysis and Random kitchen sink (RKS) based approach for offensive language detection. We explore the effectiveness of Google sentence encoder, Fasttext, Dynamic mode decomposition (DMD) based features and Random kitchen sink (RKS) method for offensive language detection. From the experiments and evaluation we observed that RKS with fastetxt achieved competing results. The evaluation measures used are accuracy, precision, recall, f1-score.
In this digital era, online discussions and interactions has become a vital part of daily life of which a huge part is covered by social media platforms like twitter, facebook, instagram etc. Similar to real life there exist anti-social elements in the cyberspace, who take advantage of the anonymous nature in cyber world and indulge in vulgar and offensive communications. This includes bullying, trolling, harassment BIBREF0, BIBREF1 and has become a growing concern for governments. Youth experiencing such victimization was recorded to have psychological symptoms of anxiety, depression, loneliness BIBREF1. Thus it is important to identify and remove such behaviours at the earliest. One solution to this is the automatic detection using machine learning algorithms. Detecting offensive language from social media is a challenging research problem due to the different level of ambiguities present in the natural language and noisy nature of the social media language. Moreover, social media subscribers are from linguistically diverse and varying communities. Overseeing the complication of this problem, BIBREF2 organized a task in SemEval2019, Task 6: Identifying and Categorizing Offensive Language in Social Media. The tweets were collected by the organizers using Twitter API and have annotated them in a hierarchical manner as offensive language present in the tweet, type of the offense and target of the offense. There were three sub-tasks according to the hierarchy of annotation: a) To detect if a post is offensive (OFF) or not (NOT), b) To Identify the type of offense in the post as targeted threat (TTH), targeted insult (TIN), untargeted (UNT), c) To identify if offense is targeted to organization or entity (ORG), group of people (GRP), individual (IND), or other (OTH). The dataset had the following challenges: Dataset was comparatively smaller. Dataset was biased/imbalanced BIBREF3. In this paper, we are proposing a comparative analysis for the sub-task A :Offensive language identification of the SemEval2019, Task 6. Sub-task A was the most popular sub-task among the three and had total 104 team participation. In Table TABREF19, the list of first 5 participants along with system and f1-score has been shown. Offensive language detection is one of the challenging and interesting topic for research. Recent past had multiple shared tasks and research on this topic. One of the initial work on offensive language using supervised classification was done by Yin et al. BIBREF4. They have used Ngram, TFIDF and combination of TFIDF with Sentiment and Contextual as Features. Schmidt and Wiegand BIBREF5 gave a survey on automatic hate speech detection using NLP. The authors surveyed on features like Simple Surface Features, Word Generalization, Linguistic Features, Knowledge-Based Features, Multimodal Information etc. In 2013, a study on detecting cyberbullying in YouTube comments was done by Dadvar et al. BIBREF6. They have used a combination of content-based, cyberbullying-specific and user-based features and showed that detection of cyberbullying can be improved by taking user context into account. Shared task GermEval 2018 organised by Wiegand et al.,BIBREF7 was focused on offensive language detection on German tweets. It had a dataset of over 8,500 annotated tweets and was trained for binary classification of offensive and non-offensive tweets. They obtained an overall maco-F1 score of 76.77%. Another shared task on Aggression Identification in Social Media was organised by Kumar et al., BIBREF8. The task provided a dataset with 15,000 annotated Facebook posts and comments in Hindi and English. They obtained a weighted F-score of 64% for both English and Hindi. The rest of the paper is structured as follows. Section 2 explains about the methodology with formulation. Section 3 discusses on the Proposed approach. Section 4 talks on the Experiments and discussions performed. Finally, conclusion is given in Section 5.