A mrita_ CEN at S em E val-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending Language (2022)

Abstract: This paper narrates the work of the team Amrita_CEN for the shared task on Patronizing and Condescending Language Detection at SemEval 2022. We implemented machine learning algorithms such as Support Vector Machine (SVV), Logistic regression, Naive Bayes, XG Boost and Random Forest for modelling the tasks. At the same time, we also applied a feature engineering method to solve the class imbalance problem with respect to training data. Among all the models, the logistic regression model outperformed all other models and we have submitted results based upon the same.

## introduction discriminatory language on the social media is lately creating hostile environment towards the vulnerable communities especially women and minorities. these are reflected in day to day conversations happening on popular social media sites. it is a high time now to build a technological solution to counter the discrimination against vulnerable communities. here in this task, we consider one such issue known as "patronizing and condescending language (pcl) detection". when someone's language conveys a pompous attitude toward others or portrays them or their circumstances in a compassionate manner, eliciting feelings of sympathy and compassion, they are patronising or condescending. this is why it is important to develop a computational model to predict whether there is patronizing content in social media or not @xcite . this challenge can be solved by the applying natural language processing (nlp) concepts. the social media platforms reaches a huge audience, which might contribute to increased exclusion and inequity among vulnerable groups. despite the fact that harmful language behaviour (such as hate speech, abusive language, fake news, rumour propagation, or disinformation) @xcite , @xcite has been extensively investigated in nlp, pcl has remained a neglected field of research. we implemented seven machine learning models which include three classical machine learning algorithms and four ensemble models: support vector machine (svm), logistic regression, naive bayes, xg boost and random forest for modelling the tasks @xcite , @xcite , @xcite . the class imbalance problem was dealt by a minority oversampling technique called smote and comparative analysis of our algorithm was done by various evaluation metrics such as precision, recall and f1 score. the remaining parts of the paper are described as follows: section 2 contains dataset description along with works related to that. section 3 describes the system overview. section 4 explains the experimental setup. section 5 discusses result and the paper is concluded in section 6. ## related works this section provides a brief review of the literature published for the detection of various offensive and abusive contents pertained to violence, cyberbullying etc. shared on the social media. adithya et.al @xcite analysed the hate speech data in code-mixed form and proposed classification models for the detection. they created a dataset consisting of hindi-english codemixed tweets. machine learning algorithms like svm, random forest were used for the classification of tweets into different categories. conroy et.al @xcite reported the problem of fake news detection in their paper and their study offered a classification of different types of truthfulness evaluation methods that fall into two categories: linguistic cue with machine learning and network analysis approaches. zampieri et al @xcite predicted the nature and victim of offensive content shared on social media. they used the of-fensive language identification dataset (olid) for the analysis. they compared the performance of different machine learning models on this dataset. wang and potts @xcite used a corpus called talkdown for detecting the condescension in a text by incorporating the context. the dataset consist of annotated social media messages. they explored the issue of modelling condescension in direct communication from an nlp perspective. they used bert-based models for developing the baseline models. ## results in both the sub tasks we used three classical ml models and four ensemble techniques for classification. the three ml models were logistic regression in this analysis we used evaluation metrics such as precision, recall and f1 score. the official evaluation metric was f1 score for positive class for subtask 1. for the validation dataset an f1 score of 0.41 was achieved for positive class and in case of test dataset an f1 score of 0.39 was obtained and our final rank for subtask 1 in the competition was 60. for subtask 2 we got a macro_average f1 score of 0.45 during the post evaluation phase. from the tables 1 and 2 we can clearly see that the macro f1 score of logistic regression stood out among all the other models. moreover the execution time for logistic regression was less compared to other models especially the ensemble techniques. hence this model was used for the final prediction of the test dataset. ## conclusion this paper narrates the work of amrita_cen with respect to semeval 2022 task 4 competition named " patronizing and condescending language detection ". a total of seven machine learning algorithms were used which include three classical ml models and four ensemble techniques. the problem of class imbalance was dealt with minority oversampling technique called smote. considering macro f1 score for both the sub tasks, logistic regression performed the best and the results were submitted using the same model. coming to the future work, implementation using deep learning and bert approaches can give better results compared to classical machine learning models.