Less Sparse Feature Set With Meta Heuristic Weighted Classifier for Tweet Sentiment Classification

Abstract

Twitter using Machine Leaning Techniques has been done. While consideration Bigram, Unigram,. SVM and naïve Bayes classifier which hybrid with PSO and ACO for effective feature weight. In Fig. 4.9 compare all experiment by on graph which shows that SVM_ACO and SVM_PSO better perform than SVM. NB_ACO and NB_PSO perform better than NB but if compare between hybrid approaches then SVM_PSO show 81.80% accuracy,85% precision and 80% recall. IN case of naïve Bayes NB_PSO 76.93% accuracy,76.24 precision and 82.55% recall, so experiments conclude that Naive Bayes improve recall and SVM improve precision and accuracy when use as hybrid approach.