Emotion Classification with Reduced Feature Set SGDClassifier, Random Forest and Performance Tuning

Abstract

Text Classification is vital and challenging due to varied kinds of data generated these days; emotions classification represented in form of text is more challenging due to diverse kind of emotional content and such content is growing on web these days. This research work is classifying emotions written in Hindi in form of poem with 4 categories namely Karuna, Shanta, Shringar and Veera. POS tagging is used on all the poem and then features are extracted by observing certain poetic features, two types of features are extracted and the results in terms of accuracy is measured to test the model. 180 Poetries were tagged and features were extracted with 8 different keywords, and 7 different keywords. The model is build with Random Forest, SGDClassifier and was trained with 134 poetries and tested with 46 Poetries for both types of features. The results with 7 keyword feature is comparatively better than 8 keyword feature by 7.27% for Random Forest and 10% better for SGDClassifier. Various combinations of hyper parameters are used to get the best results for statistical measure precision and recall for performance tuning of the model. The model is also tested with k – fold cross validation with average result 62.53% for 4 folds and 60.45% for 8 folds with Random Forest and 54.42% for 4 folds and 48.28% for 8 folds with SGDClassifier, the experimentation result of Random Forest is better than SGDClassifier on the given dataset.

Efficiency of Identification of Blackcurrant Powders Using Classifier Ensembles

“…The bagging algorithm can quickly generate multiple copies of the same classifier while learning different subsets of data and generalizing the model [ 55 , 56 ]. SGDClassifier applies a gradient optimization technique, minimizing loss functions [ 57 ]. It can be a very flexible yet effective model, especially in the classification aspect.…”