A Study of Feature Extraction techniques for Sentiment Analysis
Table 6: Accuracy of classifiers
['Datasets Classifiers', 'Dataset-4 TF-IDF', 'Dataset-4 Doc2Vec', 'Dataset-5 TF-IDF', 'Dataset-5 Doc2Vec']
To estimate the performance of TF-IDF and Doc2Vec on classification algorithms we use accuracy measure as the metric. Accuracy is calculated as the ratio of number of reviews that are correctly predicted to the total number of reviews. The accuracy is highest for Logistic Regression in case of TF-IDF whereas SVM with rbf kernel achieved highest accuracy in case of Doc2Vec. For the second dataset, TF-IDF performed better than Doc2Vec. The accuracy is highest for SVM with rbf kernel in case of TF-IDF whereas LR,SVM with rbf and linear kernels achieved highest accuracy in case of Doc2Vec. For the third dataset, TF-IDF and Doc2Vec achieved similar accuracies. SVM with rbf kernel achieved highest accuracy in case of TF-IDF whereas LR,SVM with rbf and linear kernels achieved highest accuracy in case of Doc2Vec. For the fourth dataset, Doc2Vec performed better than TF-IDF for all classifiers. The accuracy is highest for SVM with rbf kernel in case of TF-IDF whereas LR,SVM with rbf,linear kernels,Bernoulli NB achieved highest accuracy in case of Doc2Vec. For the fifth dataset, TF-IDF performed slightly better than Doc2Vec. The accuracy is highest for LR in case of TF-IDF whereas SVM with rbf kernel achieved highest accuracy for Doc2Vec. Considering the performance analysis of all the classifiers on all datasets , we conclude that Logistic regression, SVM with linear and rbf kernels performs better than all the other classifiers.