Less Sparse Feature Set with Meta Heuristic Weighted Classifier for Tweet Sentiment Classification
Ravinder Singh1, Rajdeep Kaur2
1Ravinder Singh, Computer science Engineering, Chandigarh University, India.
2Er. Rajdeep Kaur, Computer science engineering, Chandigarh University, India.
Manuscript received on December 18, 2019. | Revised Manuscript received on December 26, 2019. | Manuscript published on January 10, 2020. | PP: 1328-1334 | Volume-9 Issue-3, January 2020. | Retrieval Number: B6699129219/2020©BEIESP | DOI: 10.35940/ijitee.B6699.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Sentiment Analysis, Twitter Sentiment Analysis, Support Vector Machine
Scope of the Article: Classification