## 2 Related Work
### Toxic Speech Detection in Hindi-English Code-Mixed Text  
Researchers proposed several approaches to detecting toxic content in the code-mixed language of Hindi-English text via detecting hate tweets [8; 12; 29; 30; 31] and aggressive tweets [13]. Various algorithms were proposed by these researchers, mainly ML-based classification algorithms [12; 30] and DL-based classification algorithms [8; 13; 29]. [PERSON] et al. [12] proposed ML-based classification algorithms to introduce hate speech detection in code-mixed (Hindi-English) posts on Twitter. SVM and random forest (RF) classifiers were built. The performance of SVM was better than RF, with the highest accuracy being 71.7% when all the features were used. Considering individual features, character n-grams in SVM and word n-grams in RF achieved the highest accuracy. [PERSON] and [PERSON] [29] proposed DL-based classification algorithms to detect hate speech in code-mixed (Hindi-English) tweets on Twitter. The 3849 tweets prepared by [PERSON] et al. [12] were used for model evaluation, and a large set of tweets targeting minority groups were collected to train the domain-specific word embeddings. Domain-specific word embeddings improved the representation of hate targets much better than general embeddings. SVM and RF classifiers were built using the same methodology from [PERSON] et al. [12] as their baseline. Additionally, three DL models, namely CNN-1D, LSTM, and BiLSTM, were built using the word embeddings for comparison. CNN-1D achieved the highest precision (83.34%), F1-score (80.85%), and accuracy (82.62%), whereas BiLSTM achieved the highest recall of 78.90%. Hence, the results show that the DL models performed much better than the statistical methods in detecting the semantics and hate speech contexts. [PERSON] et al. [13] proposed ML and DL-based classification algorithms to detect aggressive speech in Hindi-English code-mixed posts and comments on Facebook. The data used had 12,000 posts/comments in Roman and Devanagari from Facebook, published as part of an online shared task by [PERSON] et al. [32], labeled into three classes: Covertly Aggressive, Overtly Aggressive, and Non-Aggressive. Six classification models were built, namely multimodal naive Bayes, decision tree, SVM, multilayer perceptron, LSTM, and CNN, using various combinations of features and parameter tuning. CNN performed the best, achieving an accuracy of 73.2%. Neural networks do not always serve better than ML algorithms in speech detection. The models could not learn appropriately since the posts and comments were not diverse and varied in content enough. [PERSON] and [PERSON] [8] proposed DL-based classification algorithms to detect hate speech in code-mixed (Hindi-English) tweets on Twitter. SVM and RF classifiers were built using the same methodology from [PERSON] et al. [12] as their baseline. Moreover, the \"sub-word level LSTM model and Hierarchical LSTM model with attention based on phonemic sub-words\" were built for comparison. SVM achieved the highest accuracy of 70.7%, whereas Hierarchical LSTM achieved the highest recall (45.1%) and F1-score (48.7%). A simple architecture of DL was used due to the limited size of the data. In the study by [PERSON] et al. [30], Facebook's pre-trained embedding, fasttext, was used as a feature matrix for classifying tweets using SVM-Linear, SVM-Radial Basis Function (RBF), and RF algorithms. Performances were then evaluated and compared using word2 vec and doc2 vec features. The fasttext feature gave the highest accuracy of 85.81%, and the word2 vec feature gave 75.11% accuracy, both using the SVM-RBF classifier, while the doc2 vec feature gave 64.15% accuracy using RF. This study has outperformed all the previous studies using fasttext with character-level features for classification, which achieved better results than document-level and word-level features. Studies in this category present different and compelling solutions for detecting toxic speech in Hindi-English code-mixed text.