A Framework for Sentiment Analysis Classification based on Comparative Study
Zahir Younis1, Nidal Kafri2, Wael Hasouneh3

1Zahir Younis, Department of Computer Science, Al-Quds University, Al-Quds, Palestine.
2Nidal Kafri*, Department of Computer Science, Al-Quds University, Al-Quds, Palestine. 
3Wael Hasouneh, Department of Computer Science, Al-Quds University, Al-Quds, Palestine. 
Manuscript received on 12 August 2021. | Revised Manuscript received on 05 April 2022. | Manuscript published on 30 May 2022. | PP: 7-15 | Volume-12 Issue-2, May 2022. | Retrieval Number: 100.1/ijsce.A35240911121 | DOI: 10.35940/ijsce.A3524.0512222
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Abstract: Number of Feature Selection and Ensemble Methods for Sentiment Analysis Classification had been introduced in many searches. This paper presents A frame work for sentiment analysis classification based on comparative study on different classification algorithms i.e., comparison between combinations of classification algorithms: Bayes, SVM, Decision Tree. We also examined the effect of using feature selection methods (statistical, wrapper, or embedded), ensemble methods (Bagging, Boosting, Stacking, or Vote), tuning parameters of methods (SVM Attribute Eval, Stacking), and the effect of merging feature subsets selected by embedded method on the classification accuracy. Particularly, the results showed that accuracy depends on the feature selection method, ensemble methods, number of selected features, type of classifier, and tuning parameters of the algorithms used. A high accuracy of up to 99.85% was achieved by merging features of two embedded methods when using stacking ensemble method. Also, a high accuracy of 99.5% was achieved by tuning parameters in stacking method, and it reached 99.95% and 100% by tuning parameters in SVM Attribute Eval method using statistical and machine learning approaches, respectively. Furthermore, tuning algorithms’ parameters reduced the time needed to select feature subsets. Thus, these combinations of algorithms can be followed as a frame work for sentiment analysis. 
Keywords: Artificial Intelligence, Sentiment Analyses, Machine Learning, Ensemble Methods, Feature Selection.
Scope of the Article: Machine Learning