Performance Evaluation of Classification Algorithm for Movie Review Sentiment Analysis
DOI:
https://doi.org/10.47839/ijc.22.1.2873Keywords:
Decision Tree, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Sentiment Analysis, Performance EvaluationAbstract
The majority of the current research on sentiment analysis, which covers topics like political reviews, movie reviews, and product reviews, has developed quickly. The classification and clustering stage of sentiment analysis research involves a number of subjects. Some of them cover text classification comparison research and algorithm performance optimization. An intricate issue in sentiment analysis research is dealing with unstructured or semi-structured data. The sentiment analysis procedure and improving the efficacy of the classifier’s algorithm are both hampered by unstructured data. In order to manage unstructured data successfully and provide accurate and relevant information, unique strategies are required. The proposed classification model performance evaluation using Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Decision Tree is specifically covered in this paper. According to the study’s findings, SVM has an accuracy rate of 96% and Naive Bayes is 86%. While the decision tree’s gain accuracy is 78 percent and the kNN classification model’s gain accuracy is 78 percent, respectively. The test results demonstrate that SVM is superior to other classification models in terms of accuracy performance.
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