A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
Table 4: Precision, recall, F1-score and support for Logistic regression (LR), Support vector machine (SVM), Decision tree (DT), Random Forest (RF), Multinomial Naive Bayes (MNB), K-nearest neighbours (KNN), DME, CDME, 1DConv, BERT.
['[EMPTY]', '[BOLD] LR Precision', '[BOLD] LR Recall', '[BOLD] LR f1-score', '[BOLD] LR support', '[BOLD] SVM Precision', '[BOLD] SVM Recall', '[BOLD] SVM f1-score', '[BOLD] SVM support']
A recall of 1.00 and precision around 0.13 for non-Malayalam class shows that all the classes have been labelled as non-Malayalam irrespectively. Other than SVM, LR, DT, RF shows considerable macro average score for precision, recall and F1-score. However, MNB and KNN achieve higher macro-averaged precision at the expense of the lower recall values. As mentioned earlier, deep learning models are using pre-trained embeddings. The use of fastText in combination with word2vec for DME and CDME gives both local as well as global context. 1DConv shows the better macro-averaged score in precision, recall and F1-score, BERT, on the other hand, fails to identify “Mixed feeling” class. However, DME and CDME succeed in identifying all the classes.