N-Gram and TF-IDF for Feature Extraction on Opinion Mining of Tweets with SVM Classifier
Jhonathan de Godoi Brandão, Wesley Pacheco Calixto · 2019 International Artificial Intelligence and Data Processing Symposium (IDAP) · 2019
This work evaluates the performance of the Support Vector Machine (SVM) classifier on tweets opinion mining in five datasets available on the literature. For feature extraction, the N-Gram and TF-IDF, k-folds cross-validation techniques were used in the classifier modeling step. Variations of N-Gram with L-gram, 2-gram, and 3-gram combined with k-folds cross-validation in 10-folds, 15-folds and 20-folds yielded 63.93% to 81.06% accuracy. Satisfactory results were obtained, which can be improved with the application of an optimization technique to adjust the classifier parameters.